Dynamic computer systems and uses thereof

ABSTRACT

The invention concerns computer systems that are specially adapted to propagate content over a dynamic network, substantially in real time, by virtue of the locational proximity of network-joined Client Computers. Preferably, the content will also be weighting (for example, proximity-weighting, rank-weighting, topic-weighting, query-weighting, time-weighting, location-weighting, locality-weighting, vote-weighting, segment-weighting, etc.). The invention particularly concerns such computer systems that employ more than one such weighting. The invention particularly concerns such computer systems that operate using, or through, mobile devices, particularly for distributed computing applications, including social media applications and communications applications conducted over Restricted Computer Networks.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/512,442 (which was filed on Oct. 12, 2014, and issued asU.S. Pat. No. 9,742,853 on Aug. 2, 2017), and which claims priority toU.S. Patent Application No. 62/000,015 (filed on May 19, 2014), each ofwhich applications is hereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

Field of the Invention

The invention concerns computer systems that are specially adapted topropagate content over a dynamic network, substantially in real time, byvirtue of the locational proximity of network-joined Client Computers.Preferably, the content will also be weighted (for example,proximity-weighted, rank-weighted, topic-weighted, query-weighted,time-weighted, location-weighted, locality-weighted, vote-weighted,segment-weighted, etc.). The invention particularly concerns suchcomputer systems that employ more than one such weighting. The inventionparticularly concerns such computer systems that operate using, orthrough, mobile devices, particularly for distributed computingapplications, including social media applications and communicationsapplications conducted over Restricted Computer Networks.

Description of Related Art

Online social media services, such as social networking sites, searchengines, news aggregators, blogs, and the like provide a richenvironment for users to comment on events of interest and communicatewith other users. Examples of social media services include: 43 Things®,Academia.edu®, Bebo®, Blauk, Blogster, Bolt.com, Buzznet®, CafeMom®,Care2®, CaringBridge®, Classmates.com®, CouchSurfing®, Diaspora*®,Eons.com, Experience Project, Exploroo, Facebook®, Faceparty®,Face.com®, Flixster®, Flickr®, Focus.com, Foursquare®, Friendica,Friends Reunited, Google+®, GovLoop®, Instagram®, Jaiku®, Lifeknot®,LinkedIn®, MEETin, Meetup®, MocoSpace®, MyHeritage®, MyLife®, My Opera®,Myspace®, Pinterest®, Plaxo®, Reddit®, ReverbNation.com, SocialVibe®,Spaces, SnapChat®, Stage 32®, Stickam®, Talkbiznow, TravBuddy.com,Travellerspoint, tribe.net, Tumblr®, Twitter®, Vimeo®, WhatsApp®,Yammer®, and Yelp®.

Proximity analysis in social networking has been used in advertising andmarketing, for example to permit merchants to more efficiently markettheir products and services to users (see, e.g., U.S. Pat. No. 8,306,921and United States Patent Publications No. 2009/0157473; 2009/0204600;2011/0196801; 2012/0084807; 2012/0084811 and 2012/0136723 and PCTPublications No. WO 2011/097510 and WO 2013/052081). Conversely, methodsof sharing multimedia have been developed that measure the distance(“hop”) between sending and receiving computers so as to provide ameasure of the level of interest in such by the members of the socialnetwork (with multimedia having greater hop-distance being indicative ofa higher level of interest) (see, e.g., U.S. Pat. No. 8,260,882 andUnited States Patent Publication No. 2009/0157845). Proximity analysishas been proposed as a means for providing enhanced securitysurveillance (see, e.g., United States Patent Publications No.2009/0292549 and 2010/0036875). Methods of information sharingassociated with an event location are disclosed in U.S. Pat. Nos.8,204,759 and 8,510,383 and in United States Patent Publications No.2011/0066743 and 2011/0131144. Social networks in which participantsseek help or the performance of other in the user's vicinity aredescribed in United States Patent Publications No 2011/0238763 and2011/0282793.

Content recommendation systems allowing users to identify friends, orrecommend content or activities to friends, in their present or plannedfuture geographic locations have been described (see, e.g., U.S. Pat.No. 8,108,414 and United States Patent Publication No. 2011/0288912;2012/0124059; 2013/0218967; 2013/0267251 and 2014/0047357; and PCTPublication No. WO 2013/126293).

Examples of computer system architecture for social networks or forlocation determination are described in U.S. Pat. Nos. 7,818,394;7,831,684; 7,844,671; 7,949,611; 8,073,807; 8,108,414; 8,219,500;8,224,727; 8,266,145; 8,311,289; 8,341,162; 8,407,282; 8,473,386;8,473,500; 8,489,516; 8,495,095; 8,504,507; 8,521,180; 8,554,868;8,566,605; 8,601,378; 8,607,146; 8,612,869 and 8,620,828 and in UnitedStates Patent Publications No. 2014/0052544; 2014/0052795 and US2013/0073473 and in European Patent Publication EP 2151793 and in PCTPublications No. WO 2013/154679; WO 2013/170082; WO 2013/181662; WO2013/184407 and WO 2013/184957. In particular, such computer systemarchitecture may have a server-centered architecture (e.g., U.S. Pat.Nos. 8,676,667; 8,695,077; 8,694,579; 8,693,982; 8,693,464; 8,683,565;8,670,414; 8,677,418; 8,676,934; 8,667,081; 8,662,386; 8,656,421;8,647,207; 8,635,499; 8,630,867; 8,599,848; 8,601,265; 8,606,930;8,615,010; 8,612,646; 8,619,822; 8,583,781; 8,577,954; 8,582,727;8,560,939; 8,571,526; etc.) or a peer-to-peer architecture (e.g., U.S.Pat. Nos. 8,694,587; 8,693,484; 8,693,431; 8,693,392; 8,693,391;8,690,050; 8,689,307; 8,688,803; 8,688,801; 8,688,789; 8,688,780;8,688,779; 8,688,111; 8,688,038; 8,687,536; 8,683,551; 8,682,495;8,677,017; 8,676,925; 8,676,882; 8,676,855; 8,676,165; 8,671,208;8,671,202; 8,671,188; etc.).

Methods of searching, sorting and grouping data and of displaying dataon computers and mobile devices in response to user-defined criteriahave also been described (see, e.g., European Patent Publications No. EP2441039; EP 2452247 and EP 2569716; U.S. Pat. Nos. 8,091,032 and8,145,637 and United States Patent Publications No. 2010/0045705;2010/0082618; 2011/0238408; 2013/0127748; 2013/0182963; 2013/0218902;2014/0046955; 2014/0052281 and 2014/0053228 and PCT Publications No. WO2010/144766; WO 2011/005318; WO 2011/119171 and WO 2011/021202).

Despite all such advances, a need remains for computer systems that arespecially adapted to propagate content over a dynamic network,substantially in real time, by virtue of the locational proximity ofnetwork-joined Client Computers. The invention is directed to this andother needs.

SUMMARY OF THE INVENTION

The invention concerns computer systems that are specially adapted topropagate content over a dynamic network, substantially in real time, byvirtue of the locational proximity of network-joined Client Computers.Preferably, the content will also be weighted (for example,proximity-weighted, rank-weighted, topic-weighted, query-weighted,time-weighted, location-weighted, locality-weighted, vote-weighted,segment-weighted, etc.). The invention particularly concerns suchcomputer systems that employ more than one such weighting. The inventionparticularly concerns such computer systems that operate using, orthrough, mobile devices, particularly for distributed computingapplications, including social media applications and communicationsapplications conducted over Restricted Computer Networks.

In detail, the invention provides a computer system for disseminatingcontent among interconnected Client Computers, wherein the computersystem comprises: a content-providing Client Computer (I) and acontent-receiving Client Computer (II) digitally interconnected with oneanother to form a distributed communications network; wherein

-   (A) the content-providing Client Computer (I) and the    content-receiving Client Computer (II) comprise: a means for    inputting data, a means for receiving content provided by a Client    Computer, a means for providing content to a Client Computer, a    means for presenting content to a user, a computer-addressable    memory for storing content and programming instructions and a    processor for processing data and for implementing the programming    instructions;-   (B) the Client Computers (I) and (II) are interconnected to one    another directly or through one or more other Client Computers; and-   (C) the content-receiving Client Computer (II) receives content from    the content-providing Client Computer (I), wherein the provided    content adjusts in response to changes in:    -   (i) a Favorability Value assigned by the content-providing        Client Computer (I); and/or    -   (ii) where the Client Computers (I) and (II) are interconnected        to one another through one or more other Client Computers, a        Favorability Value assigned by one or more of the other Client        Computers; thereby disseminating content across the distributed        network;        and wherein the Favorability Value is determined by a        Favorability Function that considers one or more Favorability        Parameter(s), with the proviso that when selected Favorability        Parameters include both vote and time, the Favorability Function        shall additionally consider one or more additional Favorability        Parameter(s).

The invention additionally concerns such a computer system wherein thecontent-receiving Client Computer (II) of the computer system storesreceived content in a Content Stack memory; wherein content stored inthe Content Stack memory rises in response to increases in FavorabilityValue of the content, and falls in the Content Stack memory in responseto decreases in Favorability Value, such that the number or amount ofcontent stored in the Content Stack memory remains within availableprocessing and bandwidth parameters.

The invention additionally concerns such a computer system wherein thecontent-receiving Client Computer (II) of the computer system presents asubset of the stored received content to its user, wherein the presentedcontent is stored in a Presentation Stack memory; wherein content storedin the Presentation Stack memory:

-   (A) rises in the Presentation Stack memory in response to:    -   (1) increased proximity between the content-receiving Client        Computer (II) and the content-providing Client Computer (I);    -   (2) increases in the Favorability Value of the content:        -   (i) assigned by the content-providing Client Computer (I);            and/or        -   (ii) assigned by the content-receiving Client Computer (II);            and/or        -   (iii) where the Client Computers (I) and (II) are            interconnected to one another through one or more other            Client Computers, assigned by one or more of the other            Client Computers; and    -   (3) changes in weighting preferences applied by the        content-receiving Client Computer (II) that increase its user's        desire for such content; and-   (B) falls in the Presentation Stack memory in response to:    -   (1) decreased proximity between the content-receiving Client        Computer (II) and the content-providing Client Computer (I);    -   (2) decreases in the Favorability Value of the content:        -   (i) assigned by the content-providing Client Computer (I);            and/or        -   (ii) assigned by the content-receiving Client Computer (II);            and/or        -   (iii) where the Client Computers (I) and (II) are            interconnected to one another through one or more other            Client Computers, assigned by one or more of the other            Client Computers; and    -   (3) changes in weighting preferences applied by the        content-receiving Client Computer (II) that decrease its user's        desire for such content; such that the number or amount of        content stored in the Presentation Stack memory of Client        Computer (II) remains within user-selected parameters.

The invention additionally concerns any of such computer systems whereinthe Favorability Parameter(s) comprise one or more of the FavorabilityParameters: dissemination, distance, hop-distance, and premium.

The invention additionally concerns any of such computer systems whereinthe Favorability Parameter(s) comprise one or more of the FavorabilityParameters: vote, dissemination, distance, hop-distance and premium.

The invention additionally concerns any of such computer systems whereinthe Favorability Parameter(s) comprise one or more of the FavorabilityParameters: dissemination, distance, hop-distance, time and premium.

The invention additionally concerns any of such computer systems whereinthe presented content is weighed based on one or more weightingpreferences selected from the group consisting of: proximity-weighting,rank-weighting, topic-weighting, query-weighting, time-weighting,location-weighting, locality-weighting, vote-weighting, andsegment-weighting.

The invention additionally concerns any of such computer systems whereina Client Computer of the computer system votes to favor or disfavor areceived content, or provides related content, and provides the vote orthe related content to another Client Computer.

The invention additionally concerns any of such computer systems whereinthe network additionally comprises a Content-Monitoring Computer.

The invention additionally concerns any of such computer systems whereinthe network comprises a Restricted Computer Network.

The invention additionally concerns any of such computer systems whereinthe means for presenting content to a user comprises a graphical userinterface.

The invention additionally concerns a computer-implemented method fordisseminating content among interconnected Client Computers, wherein themethod enables the digital interconnection of a content-providing ClientComputer (I) and a content-receiving Client Computer (II), to therebyform a distributed communications network, wherein,

-   -   (1) the content-providing Client Computer (I) and the        content-receiving Client Computer (II) comprise: a means for        inputting data, a means for receiving content provided by a        Client Computer, a means for providing content to a Client        Computer, a means for presenting content to a user, a        computer-addressable memory for storing content and programming        instructions, and a processor for processing data and for        implementing the programming instructions;    -   (2) the Client Computers (I) and (II) are interconnected to one        another directly or through one or more other Client Computers,        wherein content received from Client Computer (I) and provided        to Client Computer (II) adjusts in response to changes in:        -   (i) a Favorability Value assigned by the content-providing            Client Computer (I); and/or        -   (ii) where the Client Computers (I) and (II) are            interconnected to one another through one or more other            Client Computers, a Favorability Value assigned by one or            more of the other Client Computers;            -   thereby disseminating content across the distributed                network;            -   and wherein the Favorability Value is determined by a                Favorability Function that considers one or more                Favorability Parameter(s), with the proviso that when                selected Favorability Parameters include both vote and                time, the Favorability Function shall additionally                consider one or more additional Favorability                Parameter(s).

The invention additionally concerns such a computer-implemented methodwherein the method permits the content-receiving Client Computer (II) ofthe computer system to store received content in a Content Stack memory;wherein the method permits content stored in the Content Stack memory torise in response to increases in Favorability Value of the content, andto fall in the Content Stack memory in response to decreases inFavorability Value, such that the number or amount of content stored inthe Content Stack memory remains within available processing andbandwidth parameters.

The invention additionally concerns such a computer-implemented methodwherein the method permits the content-receiving Client Computer (II) ofthe computer system to present a subset of the stored content to itsuser, wherein the presented content is stored in a Presentation Stackmemory; wherein the method permits content stored in the PresentationStack memory:

-   (A) to rise in the Presentation Stack memory in response to:    -   (1) increased proximity between the content-receiving Client        Computer (II) and the content-providing Client Computer (I);    -   (2) increases in the Favorability Value of the content:        -   (i) assigned by the content-providing Client Computer (I);            and/or        -   (ii) assigned by the content-receiving Client Computer (II);            and/or        -   (iii) where the Client Computers (I) and (II) are            interconnected to one another through one or more other            Client Computers, assigned by one or more of the other            Client Computers; and    -   (3) changes in the weighting preferences applied by the        content-receiving Client Computer (II) that increase its user's        desire for such content; and-   (B) to fall in the Presentation Stack memory in response to:    -   (1) decreased proximity between the content-receiving Client        Computer (II) and the content-providing Client Computer (I);    -   (2) decreases in the Favorability Value of the content:        -   (i) assigned by the content-providing Client Computer (I);            and/or        -   (ii) assigned by the content-receiving Client Computer (II);            and/or        -   (iii) where the Client Computers (I) and (II) are            interconnected to one another through one or more other            Client Computers, assigned by one or more of the other            Client Computers; and    -   (3) changes in the weighting preferences applied by the        content-receiving Client Computer (II) that decrease its user's        desire for such content;        such that the number or amount of content stored in the        Presentation Stack memory of the Client Computer remains within        user-selected parameters.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits the Client Computer-selected ornetwork-selected Favorability Parameters to comprise one or more of theFavorability Parameters: dissemination, distance, hop-distance andpremium.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits the Client Computer-selected ornetwork-selected Favorability Parameters to comprise one or more of theFavorability Parameters: vote, dissemination, distance, hop-distance andpremium.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits the Client Computer-selected ornetwork-selected Favorability Parameters to comprise one or more of theFavorability Parameters: dissemination, distance, hop-distance, time andpremium.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits the presented content to be weightingbased on one or more weighting preferences selected from the groupconsisting of: proximity-weighting, rank-weighting, topic-weighting,query-weighting, time-weighting, location-weighting, locality-weighting,vote-weighting, and segment-weighting.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits a Client Computer of the computersystem to:

-   (A) (1) vote to favor or disfavor a received content, or    -   (2) contribute related content, and-   (B) provide the vote or the related content to another Client    Computer.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits the network to additionally comprisea Content-Monitoring Computer.

The invention additionally concerns any of such computer-implementedmethods wherein the method permits the network to comprise a RestrictedComputer Network.

The invention additionally concerns any of such computer-implementedmethods wherein the means for presenting content to a user comprises agraphical user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a map of interconnected Client Computers of a distributedcomputer system of the present invention.

FIG. 2 illustrates a data race.

FIGS. 3A-3B illustrate how the memory stacks of two Client Computers(Client Computers 1 of User A and Client Computer 2 of User B) change inresponse to the sharing of their content.

FIGS. 4A-4B illustrate the ability of the present invention to useproximity-weighting.

FIG. 5 presents an illustrative text input screen of a touchscreenmobile phone or tablet Client Computer device.

FIG. 6 illustrates a user interface of a touchscreen mobile phone ortablet Client Computer device.

FIG. 7 illustrates the ability of an exemplary user interface to permita user to perceive desired content.

FIG. 8 illustrates how one may manipulate an exemplary user interface toperceive desired content, showing an illustrative topic conversationamong several attendees of a lecture.

FIGS. 9A-9C illustrate the use of an exemplary user interface of atouchscreen mobile phone or tablet Client Computer device to sort andgroup content for presentation to a user. FIG. 9A shows the use of asorting graphical element to group content for presentation to a user.FIG. 9B shows the use of a drag and drop capability to group content forpresentation to a user. FIG. 9C shows the use of a graphical elementselection capability to group content for presentation to a user.

FIGS. 10A-10B illustrate the ability of the invention to uselocation-weighting to form “heat maps” of recent activity (FIG. 10A) andrank activity (FIG. 10B).

FIG. 11 shows a user interface that exploits a camera functionality of aClient Computer. Content is shown in augmented reality balloons, whichare generally localized (so as to preserve the anonymity of the contentprovider in the absence of permission), or in augmented reality calloutsthat are particularly localized to Client Computers whose users havegranted identification permissions to the recipient.

DETAILED DESCRIPTION OF THE INVENTION

The invention concerns computer systems that are specially adapted topropagate content over a dynamic network, substantially in real time, byvirtue of the locational proximity of network-joined Client Computers.Preferably, the content will also be weighted (for example,proximity-weighted, rank-weighted, topic-weighted, query-weighted,time-weighted, location-weighted, locality-weighted, vote-weighted,segment-weighted, etc.). The invention particularly concerns suchcomputer systems that employ more than one such weighting. The inventionparticularly concerns such computer systems that operate using, orthrough, mobile devices, particularly for distributed computingapplications, including social media applications and communicationsapplications conducted over Restricted Computer Networks.

I. The Preferred Computers of the Computer Systems of the PresentInvention

The preferred computers of the computer systems of the present inventionare “Client Computers” (also referred to as “clients”) that willpreferably possess a means for inputting data, means for receiving andproviding data to other interconnected Client Computers (clients) of thenetwork, computer (or machine)-addressable memory configured to storecontent and/or to store programming instructions, and a computer“processor” configured to process data (including content) and toimplement programming instructions.

The programming instructions stored in a Client Computer (or otherwiseprovided to such Client Computer) enables that Client Computer toprovide content to other Client Computers of a common network (acting asa distributed server), to receive content provided from other ClientComputer(s) of the common network, and, preferably, to store(permanently, transiently, or for a user-set time duration) contentprovided from other Client Computers that are joined to such network.The programming instructions may be a computer “App,” a firmwareprogram, a computer program stored in memory, etc.). Alternatively, theClient Computer may join and participate with a computer system of thepresent invention by accessing a website, LAN, WLAN, etc. thatcommunicates with a server.

Additionally, the programming instructions enables a Client Computer todetermine the “proximity” between such Client Computer and other ClientComputer(s) of a common network, based upon the locational position ofsuch Client Computer and the locational position of such other ClientComputer(s). Locational information may be input manually (as through an“app” or input interface), but more preferably will be determinedautomatically by the Client Computer (e.g., using an internal orexternal global positioning system (GPS) receiver, or with reference tocellular signaling towers, or with reference to a fixed land-basedinternet access point, etc.). Preferably, the set of instructions storedin the Client Computer enables such Client Computer to establish asearchable and/or sortable database of received content. Theestablishment of such a database enables each Client Computer toindependently and dynamically present to its user content that isweighted by proximity, and also by topic ranking, topic keyword, time orlocation of interest.

Preferably, such Client Computers will also have an output orpresentation capability (e.g., a video output (e.g., an LCD or LEDscreen, etc.), and/or an audio output (e.g., a speaker, tone generator,etc.) so as to enable a user of a Client Computer to perceive providedor received content. The invention, however, encompasses ClientComputers that lack such output capability (for example, ClientComputers mounted in vehicles, structures (e.g., towers), or on drones,aircraft, watercraft, etc., whose purpose is to extend the communicationrange of a computer network formed by a computer system of the presentinvention. Such Client Computers may have output capabilities, however,the possession of such capabilities by such Client Computers isoptional.

The Client Computers of the present invention will preferably be mobiledevices, such as smartphones, laptops, tablets, smartwatches (e.g., Moto360®), handheld gaming systems, optical head-mounted displays (OHMD)(e.g., Google Glass®, Oculus Rift® (Oculus/Facebook Corporation), helmetvisors, night vision goggles, vehicular heads-up displays, etc.; see,e.g., U.S. Pat. Nos. 8,594,338; 8,536,776; 8,531,418; 8,487,233;8,467,133; 8,436,788; 8,431,881; 8,384,999; 8,355,610; 8,269,159;8,267,691; 8,138,991; 8,136,170; 7,841,026; 7,800,043; 7,791,809;7,755,831; 7,710,654; 7,598,849; 7,530,704; 7,496,293; etc.). Althoughparticularly adapted for use with inter-communicating mobile devices,the Client Computers of the computer systems of the present inventionmay comprise any form of computer (including stationary desktopcomputers and servers). The communication range of the Client Computersof the present invention may be up to 50 feet, up to 100 feet, up to 250feet, up to 500 feet, up to 0.25 miles, up to 0.5 miles, up to 1 mile,up to 2 miles, up to 5 miles, up to 10 mile, up to 20 miles, up to 50miles, up to 100 miles, or more than 100 miles.

The users of the Client Computers of the present invention may beunrelated individuals, groups or entities (such as people visiting apark, groups from different organizations, stores, businesses, etc.).Alternatively, the users of the Client Computers of the presentinvention may be related individuals, groups or entities (such asmedical, security, fire or emergency personnel responding to an accidentor other incident, or military personnel engaged in a joint activity).For example, emergency or other personnel responding to an incident(e.g., a burning building, etc.) could use the present invention toremain in communication with one another automatically (andparticularly, have the ability to automatically communicate with otherresponders located near to them). Alternatively, as indicated above, theClient Computers may be installed in vehicles, structures (e.g.,towers), drones, etc., and may be unattended by any user.

II. Content Sharing by the Client Computers of the Computer Systems ofthe Present Invention

As indicated above, the computer systems of the present invention arepreferably composed of two or more Client Computers that are digitallyinterconnected and which thus form a communication network capable ofproviding “content” to, and of receiving “content” from other ClientComputers that are joined to the network (collectively, referred to as“sharing” content).

As used herein, the term “content” is intended to include a digital oranalog communication relevant to a particular “topic” (i.e., subject,event, name, etc.). Each topic may have one or more subtopics(“threads”). Content is said to be “related” if it pertains to, isabout, or relates to the same topic.

The term “content” may thus comprise one, or any combination, of:

-   A. text (e.g., a comment, opinion, remark, response, vote, text    message, symbol, letter, emoticon, etc.), provided, for example in    an ASCII, UTF-8, MIME, TXT, or other text character file. Such    textual message content will preferably comprise brief textual    messages (e.g., a textual message having no more than 500 characters    (i.e., letters, symbols, emoticons, etc.), no more than 300    characters, no more than 200 characters, no more than 150    characters, or most preferably, no more than 100 characters;-   B. sound (e.g., a voice recording, song, music, tone, musical note,    sound effect, street sound, etc.), provided, for example in a 3gp,    aac, act, AIFF, ALAC, amr, atrac (.wav), Au, awb, dct, dss, dvf,    flac, gsm, iklax, IVS, m4a, m4p, mmf, mp3, mpc, msv, ogg, Opus, ra &    rm, raw, vox, way, wavpack, wma, or other type of audio file;-   C. image (e.g., a pixel-based image, a vector image, a photograph, a    holograph, a virtual reality image, a 3D image, etc.) provided, for    example in a JPEG/JFIF, JPEG 2000, Exif, TIFF, RAW, GIF, BMP, PNG,    PPM, PGM, PBM, PNM, PFM, PAM, WEBP, an HDR Raster format, RGBE,    IFF-RGFX, JPEG XR (New JPEG standard based on Microsoft HD Photo),    TGA (TARGA), ILBM (IFF-style format for up to 32 bit in planar    representation, plus optional 64 bit extensions, DEEP (IFF-style),    AI, IMG (Graphical Environment Manager image file; planar,    run-length encoded), PCX (Personal Computer eXchange), ECW (Enhanced    Compression Wavelet), IMG (ERDAS IMAGINE Image), SID    (multiresolution seamless image database, MrSID), CD5 (Chasys Draw    Image), FITS (Flexible Image Transport System), PGF (Progressive    Graphics File), XCF (eXperimental Computing Facility format, native    GIMP format), PSD (Adobe PhotoShop Document), PSP (Corel Paint Shop    Pro), VICAR file format (NASA/JPL image transport format), HVD    (Holographic Versatile Disc), 3DM, 3DS, MAX 3DS, OBJ, A2C, B3D,    BLEND, BRS, BR6, CCP, CG, CGFX, CHR, DAE, DAZ, DSF, DWF, FACEFX,    FBX, FLT, FPF, IV, LND 3D, LWO, LWS, LXO, MA, MB, MDD, MXS, SDB,    SHP, SKP, STP, U3D, VUE, PDF or other image file; or-   D. video (e.g., a moving image, a video image, movie, etc.)    provided, for example in a 3GP, ASF, AVI, RIFF, DVR-MS, Flash Video    (FLV, F4V), IFF (first platform-independent container format),    Matroska (MKV), MJ2, QuickTime, MPEG (including MPEG-1, MPEG-2,    MPEG-TS and MPEG-4 Part 12), MP4, JPEG 2000 Part 12, Ogg, RM    (RealMedia), vrcinema3D, or other video file.    Preferably, such text, sound, image or video files will be less than    1 GB, less than 500 MB, less than less than 200 MB, less than 100    MB, less than 50 MB, less than 20 MB, less than 10 MB, less than 5    MB, less than 2 MB, less than 1 MB, less than 500 KB, less than 200    KB, less than 100 KB, less than 50 KB, less than 20 KB, or less than    10 KB in size.

As stated above, a computer of a computer system of the presentinvention has the ability to receive content from other computers of thecomputer system, and preferably also has the ability to provide contentto such other computers (i.e., the ability to “share content”). As usedherein, the term “receive content” denotes the capacity of a ClientComputer to accept, employ or otherwise access, by any means, contentthat has been provided by another Client Computer of a computer systemof the present invention. As used herein, the term “provide content”denotes the capacity to transmit, transfer, relay, broadcast, orotherwise disseminate or distribute content, by any means, so thatcontent provided by one Client Computer is, or may be made, accessibleto other Client Computers of a computer system of the present invention.Such receiving and providing capabilities permit the dissemination ofcontent by and among Client Computers. Additionally, such receiving andproviding capabilities may be used to permit Content-MonitoringComputers (as discussed below) to conduct content data-mining, or toperform content backups for Client Computers (e.g., continuously,automatically at periodic intervals, or manually upon the request of theuser of such Client Computer). Such content backups may be desirable inorder to restore unsaved content if the Client Computer exits thenetwork. Additionally, such receiving and providing capabilities can beused to permit Content-Monitoring Computers or Client Computers toobtain a log of accessed content.

Most preferably, Client Computers will be directed to present content toits user. As used herein, the term “present” content denotes displayingcontent (for example, in the case of content involving textual, image orvideo content, displaying such content on a screen, projection or othervisible output) or performing or playing content (for example, in thecase of content involving sound). Preferably, such content will beprovided in “real time” (i.e., with sufficient immediacy as to providesuch content to a recipient Client Computer at substantially the sameactual time at which such content was provided by the providing ClientComputer). Alternatively, in an embodiment in which content is stored(for example, when a non-distributed computer system having acentralized server is used), historical content can be provided,permitting a user to perceive content that had been provided at anearlier time, or during a user-selected time period in the past).

As discussed below, Client Computers may be required to obtainpermission or authorization to join a network of the present invention,and may require additional permission or authorization before being ableto provide content to other Client Computers of a network. Morepreferably, however, a Client Computer will join to a network of acomputer system of the present invention automatically upon recognizingthe presence of a second Client Computer, without any need for thegranting of permission from such Client Computer or from other ClientComputers of such computer system. Although Client Computers of acomputer system of the present invention may be directed to provide thenames or usernames of their respective users to other Client Computers,it is preferred that a user's participation in a common network beanonymous to other users. To facilitate comprehension of contentdiscussions, a Client Computer may ascribe a transient token name tocontent contributors. Thus, for example, the first user to contributecontent may be identified as “Anon01,” and the second user to contributerelated content may be identified as “Anon02,” etc. In such a manner,users corresponding to “Anon01” and “Anon02” may converse with oneanother anonymously.

III. Preferred Network Configurations of the Computer Systems of thePresent Invention

As indicated above, the computer systems of the present inventioncomprise two or more Client Computers that have been “joined to anetwork” and are thus digitally interconnected with one another. Two ormore Client Computers that are joined to the same network are referredto herein as being joined to a “common” network.

The communication networks of the present invention may be of any typeand may have any form of network architecture, including any of thefollowing: a point to point network, a broadcast network, a wide areanetwork, a local area network, a telecommunications network, a datacommunication network, a computer network, an ATM (Asynchronous TransferMode) network, a SONET (Synchronous Optical Network) network, a switchedfabric network (e.g., an INFINIBAND® switched fabric network), a SDH(Synchronous Digital Hierarchy) network, a wireless network, and awireline network. The networks of the present invention may include awireless link, such as an infrared channel, a radio frequency, or asatellite band, or may comprise, or include wired (e.g., Ethernet, fiberoptic, etc.) connections or non-wired connections (e.g., laser pulses,etc.). The network may have any topology (e.g., a bus, star, or ringtopology, etc.).

The computer systems of the present invention may be of any topologyknown to those ordinarily skilled in the art as being capable ofsupporting the operations described herein. Connections and networksincluded in the connections may comprise the internet, local networks,web servers, file servers, routers, databases, computers, servers,network appliances, cell phones or any other computing devices capableof sending and receiving data, especially digital data. The computersystems of the present invention may comprise computing devicesconnected via cables, IR ports, wireless signals, or any other means ofconnecting multiple computing devices. The individual computers of thecomputer systems of the present invention may communicate with oneanother via any communication protocol used to communicate among orwithin computing devices, including without limitation radio frequency,Bluetooth, SSL, HTML, XML, RDP, ICA, FTP, HTTP, TCP, IP, UDP, IPX, SPX,NetBIOS, NetBEUl, SMB, SMTP, Ethernet, ARCNET, Fiber Distributed DataInterface (FDDI), RS232, IEEE 802.11, IEEE 802.11a, IEE 802.11b, IEEE802.11g, IEEE 802.11n, and direct asynchronous connections, or anycombination thereof. Most preferably, a Client Computer of the presentinvention will join to a network wirelessly and participate in contentsharing with other Client Computers of the present invention utilizingany protocol or protocols used to communicate among mobile devices,including AMPS, TDMA, CDMA, GSM, EDGE, GPRS or UMTS.

The networks of the present invention may be either “non-distributednetworks” or “distributed networks.” In the “non-distributed networks”of the present invention, Client Computers provide content to acentralized computer (such as a server), and receive content from thecentralized server. Individual Client Computers play no role indisseminating content; such dissemination is accomplished when otherClient Computers access the centralized computer and receive providedcontent from the centralized computer. In contrast, in the “distributednetworks” of the present invention, each Client Computer joined to thenetwork mediates the dissemination of received content to all otherClient Computers joined to that network (and more preferably to allother Client Computers joined to any network) that are withincommunication range, preferably without any gateway node. Thedissemination of content by the distributed networks of the presentinvention is accomplished by having a receiving Client Computerre-provide such content to such other Client Computers. Thus, in anon-distributed network of the present invention, content flows fromClient Computers to a centralized computer and then from the centralizedcomputer to other Client Computers and the virtual proximity of ClientComputers (i.e., their connectivity to the same central computer)determines their ability to share content. In the distributed networksof the present invention, content flows across the network by “hopping”from one Client Computer to another, preferably without any gatewaynode, and the locational proximity of Client Computers determines theirability to share content. Distributed networks are the preferrednetworks of the present invention.

Distributed networks have been previously described in the context of“ad hoc” or “mesh” networks (see generally, Ahtiainen, A. et al. (2009)“Awareness Networking In Wireless Environments,” Vehicular Technol. Mag.IEEE 4(3):48,54; Li, J. et al. (2001) “Capacity of Ad Hoc WirelessNetworks,” Proc. 7^(th) ACM Intl. Conf on Mobile Computing andNetworking, Rome, Italy, July 2001 (1-9); Broch, J. et al. (1998) “APerformance Comparison Of Multi-Hop Wireless Ad Hoc Network RoutingProtocols,” MobiCom '98 Proceedings of the 4^(th) Annual ACM/IEEE Intl.Conf on Mobile Computing and Networking, pages 85-97; Niazi, M. et al.(2009). “Agent based Tools for Modeling and Simulation ofSelf-Organization in Peer-to-Peer, Ad Hoc and other Complex Networks,Feature Issue,” IEEE Commun. Mag. 47(3):163-173; Lee, S.-B. et al.(2013) “Content Management In A Mobile Ad Hoc Network: BeyondOpportunistic Strategy,” Intl. J. Commun. Networks Distributed Syst.10(2):123-145; Steudi, P. et al. (2008) “Demo Abstract Ad Hoc SocialNetworking using MAND,” Proc. 14^(th) Intl. Conf on Mobile Computing andNetworking (MobiCom '08) pp. 1-3; Li, D. et al. (2013) “Opinion ImpactModels and Opinion Consensus Methods in Ad Hoc Tactical SocialNetworks,” Discrete Dynamics in Nature and Society (2013): Article ID758079, pp. 1-6). However, the utility of such prior networks has notbeen fully established. Network flow has been deemed to be limitedbecause the throughput available to each single node's applications islimited by the forwarding load imposed by distant nodes (Li, J. et al.(2001) “Capacity of Ad Hoc Wireless Networks,” Proc. 7^(th) ACM Intl.Conf on Mobile Computing and Networking, Rome, Italy, July 2001 (1-9)).To address network flow issues, previously described ad hoc or meshnetworks are configured to contain gateway nodes (see, e.g., U.S. Pat.Nos. 8,570,990; 8,690,381; 8,654,713; 8,681,655 and 8,638,686), or mayrestrict the ability of nodes to send data in real time (see, e.g., U.S.Pat. Nos. 6,850,511; 7,002,944; and 8,625,544), or may involve specificpacket routing protocols (see, e.g., U.S. Pat. No. 8,582,502) or involveother restrictions.

A. Operation of a Preferred Distributed Network of the Present Invention

As described above, the Client Computers of the computer system of thepresent invention preferably form a distributed network in whichcontent, provided from one Client Computer, is received by a secondClient Computer, and provided from such second Client Computer toadditional Client Computers, such that a network map comprises sets ofClient Computer nodes that are each in communication with interlockingsubsets of the Client Computers of the distributed network, in which thearea covered by a particular subset reflects the communication range ofthe Client Computers (FIG. 1). FIG. 1 shows a map of interconnectedClient Computers; for simplicity of illustration only three subsets ofClient Computers are shown (dashed circles). As will be appreciated,however, each Client Computer serves as the node of a subset, such thata large number of interlocking subsets are formed. Client computerswithin a particular subset (e.g., the Client Computers of User A andUser B in Subset 1, or the Client Computers of User B and User C inSubset 2) are capable of providing and receiving content to/from oneanother. Thus, although User C is “out of range” of User A, theinvention permits User C and User A to nevertheless share content withone another, through the Client Computer of User B. User B is withinboth Subset 1 and Subset 2, and is thus in range of both User A and UserC. Likewise, the distributed network formed by the present inventionpermits content to be exchanged and shared among all members of anetwork, even those located most distant from one another (e.g., User Aand User Z).

This aspect of the present invention permits its distributed network tobe “dynamic.” The distributed network dynamically and automaticallyadjusts to continue to provide content to the Client Computers of thenetwork. For example, in FIG. 1, the Client Computers of User A and UserC are able to share content with one another, through the ClientComputer of User B. However, if User A were to move to be next to UserZ, he would, throughout such journey, be continuously able to maintaincontent sharing with User C, through the Client Computers of otherusers. Indeed, within the communication range of their respective ClientComputers, all of the users shown in FIG. 1 could move about freelywithout altering their content sharing ability or the content sharingabilities of any other user. Provided that at least one Client Computeris within communication range of another Client Computer, the “coverage”of their common network can dynamically grow, alter or migrate inresponse to changes in the locational positions of all other ClientComputers. Thus, a salient benefit of the distributed networks of thepresent invention is that content provided by a user traverses acrossthe entire network (“content flow”) so as to be accessible to all otherusers of the network.

Users may elect to not perceive particular content, or they may elect toview content relevant to topics of interest. However, because theselected content is stochastic and is carried in the network flow forcontent that a user has selected as being of interest, any respondingcontent creates a potential for a “data race.” The occurrence of a datarace reflects the capability that the present invention provides forClient Computers to “weight” received content. Users may thus “vote” topromote or disfavor a content that is received on their ClientComputers, which then sends a message to other Client Computers,updating the vote value of such content.

As a consequence, a subsequent user may receive conflicting data withwhich to weight content (FIG. 2). FIG. 2 illustrates a data race. Asshown in FIG. 2, User A provides content to the network. The content isreceived by Users B, C and D, who each vote (passively, in the case ofUser B, and actively, in the case of Users C and D). Client computers ofall three users provide content to User D. In a data race, the firstClient Computer to provide content to User D's Client Computer sets theweighting for the content. A data race may also result from theprovision of dual (or multiple) versions of content that has developeddiscrepancies in the course of being routed over the network. Forexample, the same content may be provided to other Client Computers ofthe network (as it is being routed from one Client Computer to anotheracross the network). A Client Computer may therefore, for example,receive content having n votes from one Client Computer, and the samecontent, but with m votes, from a different Client Computer.

A data race is not a desired means of distributing content across thenetworks of the present invention. To address and resolve such data raceissues, it is preferred for each Client Computer (and/or each user) tohave a unique identifier coding. Preferably, each content provided tothe network will also have a unique identifier coding. Preferably,therefore, when a user votes to promote or disfavor a particularcontent, the identifier coding of the user or Client Computer isattached to the comment, and saved on the user's Client Computer. When“duplicate” versions of a content are received, or when votes regardingsuch content is received, the user's Client Computer takes the union ofthe data, such that a new version is created consisting of the contentand the union of votes (by user or Client Computer identifier coding)between the incoming vs. existing versions of the comment). This newversion is then provided to other Client Computers, and the processrepeats (until the content “dies” from lack of vote or distributingClient Computers). Thus, in the data race scenario of FIG. 2, the User DClient Computer will take the union of the incoming votes in order todetermine the weighting to be used by User D, and to be propagated toother Client Computers by User D. This attribute of the presentinvention also permits a user to change his/her vote on a comment andallows the computer system to compensate correctly for that change. Dueto this scheme, there will be at most N versions of the same contentbroadcast at once (but only 1 broadcast per device per content), where Nis the number of Client Computers, in accordance with the Bellman-FordAlgorithm, which proves the stability of the system (Bellman, R. (1958).“On A Routing Problem,” Quarterly of Applied Mathematics 16:87-90; Ford,L. R., Jr. (1956) “Network Flow Theory,” Paper P-923. Santa Monica,Calif.: RAND Corporation). Most preferably, in calculating the union ofthe data, the Client Computer will weight the data by the proximity ofthe content-providing Client Computers, such that votes cast by proximalusers will be counted more heavily than votes cast by more distantusers.

Alternatively, data race issues may be resolved without requiring andstoring unique user identifier codings. In such a scenario, each ClientComputer stores as N_(F), the number of received favorable votes, and asN_(D) the number of received disfavorable votes. When “duplicate”versions of a content are received, or when votes regarding such contentis received, the user's Client Computer compares newly receivedfavorable votes with the stored favorable vote tally, and stores themaximum number of favorable votes as N_(F). The Client Computer likewisecompares newly received disfavorable votes with the stored disfavorablevote tally, and stores the maximum number of disfavorable votes asN_(D). When the Client Computer then provides such content to otherClient Computers, it provides such N_(F) and N_(D) values as well.

Since a distributed network involves the provision of data amongpotentially large numbers of Client Computers, its bandwidthrequirements can increase exponentially with the number of ClientComputers and the number of content topics being provided. A furtherattribute of the computer systems of the present invention are theirability to control such bandwidth requirements. This is preferablyaccomplished through the formation and use of a “Content Stack.” In thisregard, a Client Computer will preferably be limited toproviding/re-providing K content per iteration, where K is a positiveinteger set by the user in light of the processing speed, memoryattributes and bandwidth accessibility of his/her Client Computer. Whena new content is received, such content is sorted into the Content Stackby its weighting on the receiving Client Computer, and, if necessary dueto memory or processing constraints, the least valued content of theContent Stack is dropped from Client Computer's Content Stack. It ispreferred that valued content (i.e., content that is presented to theClient Computer's user) be stored separately on a user's Client Computer(e.g., in the Client Computer's Presentation Stack) so that it will notbe erroneously removed. Content that exceeds K content per iteration maybe provided to other Client Computers, if bandwidth is not limited.Preferably such additional content will be provided only if bandwidthand/or processing capacity are not limiting for, so as to promotedynamic interactions among Client Computers, rather than a“Twitter®”-like, serial interaction (Twitter, Inc.). The Content Stackdiffers from the Presentation Stack (described below) in size (typicallyholding more topics than is presented to the user in the PresentationStack), and content (typically holding content established by theNetwork, for example in accordance with the Network's General Terms ofService (or in the case of a Restricted Network, in accordance with theRestricted Network's Terms of Service) terms of service, whereas thePresentation Stack holds content in accordance with Client Computerpreferences). The Content Stack will preferably store 50, 100, 200, 500,1000, 5000 or more contents.

In a preferred embodiment, the Client Computers of a computer system ofthe present invention will store received content in their respectiveContent Stack memories such that the amount of content (e.g., the numberof topics, or the amount of memory allocated to content storage) mayvary in response to bandwidth and/or processing capacity. As contentfills the Content Stack memory of a Client Computer, the lowest stackcontents are pushed down until they fall out of the stack and are nolonger disseminated to the network by that Client Computer.

A received content is initially ordered in the Content Stack memory of areceiving Client Computer based upon its “Favorability Value,” (see,e.g., Bardala, V. et al. (2010) “A Novel Learning Based Solution ForEfficient Data Transport In Heterogeneous Wireless Networks,” WirelessNetworks 16(6):1777-1798; Ramana, B. V. et al. (2008) “A Novel LearningBased Solution for Efficient Data Transport in Heterogeneous WirelessNetwork,” High Performance Computing—HiPC 2008 Lecture Notes in ComputerScience 5374:402-414; Bellavista, P. et al. (2007) “Context-AwareHandoff Middleware For Transparent Service Continuity In WirelessNetworks,” Pervasive and Mobile Computing 3(4):439-466) with contenthaving a higher Favorability Value placed above content having a lowerFavorability Value.

The Favorability Value of content according to the present invention isdetermined by a “Favorability Function” (“F_(Favorability)”), which is afunction of a set of Favorability Parameter Functions relating to“Favorability Parameters” that the network has elected to consider asrelevant to the Favorability Value. Thus, the Favorability Value of acontent (e.g., Content N) is determined from the Favorability Function'sconsideration of Client Computer-selected or network-selectedFavorability Parameters relating to such content:

$F_{{Favorability}_{{Content}\mspace{14mu} N}} = {f\begin{Bmatrix}{f_{1^{st}\;{Favorability}\mspace{11mu}{Parameter}}\left( {1^{st}\mspace{14mu}{Favorability}\mspace{14mu}{Parameter}} \right)} \\{f_{2^{nd}\;{Favorability}\mspace{11mu}{Parameter}}\left( {2^{nd}\mspace{14mu}{Favorability}\mspace{14mu}{Parameter}} \right)} \\{f_{3^{r\; d}\mspace{11mu}{Favorability}\mspace{11mu}{Parameter}}\left( {3^{rd}\mspace{14mu}{Favorability}\mspace{14mu}{Parameter}} \right)} \\\vdots \\{f_{4^{th}\mspace{11mu}{Favorability}\mspace{11mu}{Parameter}}\left( {n^{th}\mspace{14mu}{Favorability}\mspace{14mu}{Parameter}} \right)}\end{Bmatrix}}$

The Favorability Function may be static, or may be updated. Such updatesmay be in real-time to address network flow and capacity constraints orother network or business/commercial objective(s).

Examples of Favorability Parameter Functions include:

-   f_(vote) a function of the Favorability Parameter: vote, that    weights the number of favorable/disfavorable votes that a content    has received; for example, such a function might increase in value    as the relative number of favorable votes for a content increases;-   f_(dissemination) a function of the Favorability Parameter:    dissemination, that weights the extent of the content's    dissemination across the network (e.g., the number of hops made by    such content); for example, such a function might increase in value    as the extent of such content's dissemination increases;-   f_(distance) a function of the Favorability Parameter: distance,    that weights the distance between the content originator and the    receiving Client Computer; for example, such a function might    decrease in value as the distance between the content originator and    the receiving Client Computer increases;-   f_(hop-distance) a function of the Favorability Parameter:    hop-distance, that weights the distance between the Client Computer    providing such content and the receiving Client Computer; for    example, such a function might increase in value as the distance    between the content originator and the receiving Client Computer    decreases;-   f_(time) a function of the Favorability Parameter: time, that    weights the time interval between the time at which the content was    originated and the time at which such content is received by the    receiving Client Computer;-   f_(premium) a function of the Favorability Parameter: premium, that    weights the premium enhanced favorability that content may acquire    from commercial, governmental, social or other weightings (for    example, corporate sponsorship of a content, advertising, emergency    warnings, etc.); and-   f_(segment) a function of the Favorability Parameter: segment, that    presents content meeting network-selected “segmenting” criteria. For    example, content may be segmented to provide higher weight to    content being disseminated (or favorably voted upon) by users of (or    for) a certain age (e.g., children, the elderly, young adults,    etc.), and/or by users having a certain income level or income    threshold, and/or by users at a particular timedate (e.g., today,    yesterday, a week or month ago, 2 hours earlier, etc., or at a    particular selected time, e.g., Mar. 1, 2018 at 16:00 PST), and/or    by users at a selected location (when accessing the content), and/or    by users of a selected locality (home region), and/or by users of a    selected identification (gender, ethnicity, race, locality, etc.).    The present invention envisions the use of these, and/or any other,    desired segmenting criteria in any combination or permutation. Thus,    for example, the network could employ an f_(segment) Favorability    Parameter Function to identify content of interest to young girls,    retired male senior citizens, French-speaking children, exotic car    owners, etc.    such that an illustrative Favorability Function for a Content N    would be:

$F_{Favorability} = \begin{Bmatrix}{f_{vote}\mspace{11mu}\left( {{Number}\mspace{14mu}{of}\mspace{14mu}{Favorable}\mspace{14mu}{Votes}\mspace{14mu}{Received}\mspace{14mu}{by}\mspace{14mu}{Content}\mspace{14mu} N} \right)} \\{f_{dissemination}\;\left( {{Number}\mspace{14mu}{of}\mspace{14mu}{Clients}\mspace{14mu}{Disseminating}\mspace{14mu}{Content}\mspace{14mu} N} \right)} \\{f_{distance}\;\left( {{Distance}\mspace{14mu}{from}\mspace{14mu}{Content}\mspace{14mu} N\mspace{14mu}{Origination}\mspace{14mu}{to}\mspace{14mu}{Receivin}} \right.} \\\left. {{Client}\mspace{14mu}{Computer}} \right) \\{f_{{hop} - {distance}}\;\left( {{Distance}\mspace{14mu}{of}\mspace{14mu}{Hop}\mspace{14mu}{from}\mspace{14mu}{Providing}\mspace{14mu}{Client}\mspace{14mu}{to}\mspace{14mu}{Receiving}} \right.} \\\left. {{Client}\mspace{14mu}{Computer}} \right) \\{f_{time}\;\left( {{Time}\mspace{14mu}{Interval}\mspace{14mu}{from}\mspace{14mu}{Content}\mspace{14mu} N\mspace{14mu}{Origination}\mspace{14mu}{to}\mspace{14mu}{Time}\mspace{14mu}{of}} \right.} \\\left. {{Receipt}\mspace{14mu}{by}\mspace{14mu}{Receiving}\mspace{14mu}{Client}\mspace{14mu}{Computer}} \right) \\{f_{premium}\;\left( {{Premium}\mspace{14mu}{Favorability}\mspace{14mu}{Enhancement}\mspace{14mu}{of}\mspace{14mu}{Content}\mspace{14mu} N} \right)} \\{f_{segment}\;\left( {{Segmenting}\mspace{14mu}{{Criterion}/{Criteria}}\mspace{14mu}{Selected}\mspace{14mu}{by}\mspace{14mu}{User}\mspace{14mu}{or}}\mspace{14mu} \right.} \\\left. {Network} \right) \\{\mspace{59mu}\left\lbrack \begin{matrix}{f_{age}\;\left( {{Age}\mspace{14mu}{of}\mspace{14mu}{User}} \right)} \\{\mspace{14mu}{f_{income}\;\left( {{Income}\mspace{14mu}{of}\mspace{14mu}{User}} \right)}} \\{\mspace{31mu}{f_{timedate}\;\left( {{TimeDate}\mspace{14mu}{of}\mspace{14mu}{User}\mspace{14mu}{Vote}} \right)}} \\{\mspace{45mu}{f_{location}\;\left( {{Location}\mspace{14mu}{of}\mspace{14mu}{User}} \right)}} \\{\mspace{56mu}{f_{locality}\;\left( {{Locality}\mspace{14mu}{of}\mspace{14mu}{User}} \right)}} \\{\mspace{59mu}{f_{identification}\mspace{11mu}\left( {{Identification}\mspace{14mu}{of}\mspace{14mu}{User}} \right)}} \\{\mspace{76mu}\vdots} \\{\mspace{85mu}{f_{{segment}_{n}}\mspace{11mu}\left( {{Segment}\mspace{14mu}{Criterion}\mspace{14mu} n} \right)}}\end{matrix} \right.} \\{\mspace{31mu}\vdots} \\f_{n}\end{Bmatrix}$

The Favorability Parameter Functions employed in a Favorability Functionmay independently be linear, logarithmic, exponential, etc. For example,an f_(vote) function might be applied to a content such that thefunction's value for such content would increase linearly orexponentially in response to increasing numbers of favorable votes,thereby increasing its dissemination. An f_(dissemination) functionmight be applied to a content such that the function's value for suchcontent would increase linearly or exponentially in response to theextent of the content's dissemination across the network, but thendecrease so as to allow new content to traverse the network. Anf_(distance) function might be employed that would apply an inverselinear function, or a negative exponential function, to content, suchthat the function's value for such content would decrease to reflect thedistance between the content originator and a receiving Client Computer.An f_(hop-distance) function might be applied to a content such that thefunction's value for such content could increase linearly orexponentially as the distance between the content originator and thereceiving Client Computer decreases, but then level off or decrease forcontent having short hop distances or substantially unchanging hopdistances. An f_(time) function might be applied to a content such thatthe function's value for such content would increase, possibly rapidly(e.g., linearly or exponentially), but then level off or decrease overtime (e.g., inverse linear, negative exponential, logarithmic, etc., soas to foster the dissemination of new content and the non-disseminationof older content. An f_(premium) function might be applied to a contentin order to “outweigh” completely or partially, other FavorabilityParameter Functions. As will be recognized, by selection of FavorabilityParameters, and Favorability Parameter Functions, the present inventionpermits the Favorability Function to be set and fine-tuned to addressbandwidth and issues of network flow.

An f_(segment) function might be applied to content in order to presentto a user content being disseminated that has been judged (e.g., by theNetwork or by other users) to be of particular interest to children, theelderly, those having a certain income level, etc. The function may beapplied to past or current network traffic to assess and evaluate whatcontent had been, or is currently being, judged most relevant (e.g.,most favorably) or least relevant (e.g., least favorably) by selectedmarket segments of users (e.g., by population, age, income, ethnicity,etc.). In the most preferred embodiment, user information, such as age,income, identification, etc. would be anonymously provided and bederived from the user's profile or preferences as entered into andstored on the user's Client Computer.

Thus, as (or if) a particular content is re-received by a ClientComputer, the position of that content in the Content Stack memory ofsuch Client Computer may change to reflect the updated FavorabilityValue of that content on the network. For example, re-received contentthat is found to have acquired a higher (or lower) Favorability Valuebut which has had less dissemination can be accorded a higher ContentStack position than such content would have been accorded had it hadgreater dissemination. The Favorability Function ensures that a contentconverges (i.e., does not “crisscross” indefinitely (and exponentially)across the network until it consumes all bandwidth). As the extent ofits dissemination increases, the function causes the Favorability Valueof a content to decrease until it ultimately falls from the ContentStack memory.

Thus, in sum, content stored in Content Stack memory rises in responseto increases in the Favorability Value of the content, and falls in theContent Stack memory in response to decreases in its Favorability Value,such that the number or amount of content stored in the Content Stackmemory and provided to other Client Computers of the network remainswithin available processing and bandwidth parameters. Such parametersmay additionally use an f_(premium) Favorability Parameter Function toprioritize (i.e., overweight) content received by a Client Computer thatis provided by sponsors (e.g., commercial sponsorships, advertisements,etc.) or from a Restricted Computer Network, such that such sponsoredcontent or such received Restricted Computer Network content may bepreferentially provided to other Client Computers and thuspreferentially disseminated across the distributed network.

Consistent with the above exposition, the invention encompasses anycombination of Favorability Parameters. However, it is preferred thatFavorability Value is determined by a Favorability Function thatconsiders one or more Favorability Parameter(s), with the proviso thatwhen selected Favorability Parameters include both vote and time, theFavorability Function shall additionally consider one or more additionalFavorability Parameter(s).

Preferably, a Client Computer of a computer system of the presentinvention will select from the received content of its Content Stackmemory, valued or desired content that is to be presented to the user ofthat Client Computer. Such valued or desired content will preferably bestored in the Client Computer's memory in a “Presentation Stack” memory,such that the amount of content (e.g., the number of topics, or theamount of memory allocated to content storage) is user-controlled andvaries in response to user-selected parameters (such as a user's contentweighting preferences) and received new content. Content stored inPresentation Stack memory is preferably determined by the proximitybetween the content-receiving Client Computer and a content-providingClient Computer that is providing such content, by the FavorabilityValue of the content and by the weighting preferences being applied bythe content-receiving Client Computer, such that the number or amount ofcontent stored in the Presentation Stack memory of the Client Computerremains within user-selected parameters. A Client Computer of thepresent invention may have a single Presentation Stack memory, or mayhave 2, 3 or more than 3, separate Presentation Stacks memory. Forexample, content in a “first” language may be stored in a “first”Presentation Stack memory, and content in a “second” language may bestored in a “first” Presentation Stack memory.

Content stored in Presentation Stack memory thus:

-   -   (A) rises in the Presentation Stack memory in response to:        -   (1) increased proximity between the content-receiving Client            Computer and a content-providing Client Computer that is            providing such content;        -   (2) increases in the Favorability Value of the content; and        -   (3) changes in the weighting preferences applied by the            content-receiving Client Computer that increase the user's            desire for such content; and    -   (B) falls in the Presentation Stack memory in response to:        -   (1) decreased proximity between the content-receiving Client            Computer and a content-providing Client Computer that is            providing such content;        -   (2) decreases in the Favorability Value of the content; and        -   (3) changes in the weighting preferences applied by the            content-receiving Client Computer that decrease the user's            desire for such content;    -   such that the number or amount of content stored in the        Presentation Stack memory of the Client Computer remains within        user-selected parameters.

It will be understood that although for ease of reference, the presentinvention refers to such memory as “stacks,” the term is intended togenerally encompass and denote a database of any structure in which aparticular content is associated with such proximity, Favorability Valueand weighting preferences.

Most preferably new topics (i.e., topics that were not previouslyprovided to a Client Computer) comporting with the weighting preferencesof the user will be initially placed at the top of the PresentationStack upon their receipt, pending a user-provided vote to favor ordisfavor such content. The Presentation Stack will preferably store the1, 5, 10, 20, 50, 100, etc. most discussed topics received by the ClientComputer.

Although the Content Stack and Presentation Stack may be separatelystored, it is preferable to combine both stacks to form a single memorystack that internally distinguishes presented content from other storedcontent.

FIGS. 3A-3B illustrate how the memory stacks of two Client Computers(Client Computers 1 of User A and Client Computer 2 of User B) change inresponse to the sharing of their content. As illustrated in FIG. 3A,both Client Computers have been instructed to display only 5 topics(i.e., their Presentation Stacks are both set to display only the mostvalued or desired 5 contents). Content from Client Computer 1 isprovided to Client Computer 2, but, in accordance with User B'sinstructions, Client Computer 2 only presents topics that have beenweighted. Non-presented content is preferably stored in Client Computer2's memory (shown grayed in FIG. 3A) so that it may be readily presentedto User B should User B's weighting preferences change. Likewise,content from Client Computer 2 is provided to Client Computer 1, andtopics are presented to User A in accordance with User A's weightingpreferences, and non-presented content is preferably stored in ClientComputer 1's memory (shown grayed in FIG. 3A) so that it may be readilypresented to User A should User A's weighting preferences change. FIG.3B illustrates how the respective stacks of Client Computer 1 and ClientComputer 2 are changed by content sharing between them. New content isadded to the top of the stack, and existing content is repositioned inthe stack, or dropped from the presented content of the stack,reflecting the respective user's weighting preferences.

Each favorable vote received by a particular content enhances itsrank-weighting and thus its ability to flow across the network.Likewise, receipt of disfavoring votes diminishes the rank-weighting ofthe content, and thus decreases its ability to flow across the network.Thus, content having higher favorable votes will flow further thancontent having higher disfavorable votes. As discussed above, a ClientComputer may be configured to present its user with content pertainingto the 1, 5, 10, 20, 50, 100, etc. most discussed topics received by theClient Computer. As a particular content receives additionaldisfavorable votes (or as other content receive additional morefavorable votes), such particular content will fall in ranking until itis eventually dropped from the stack of content presented to the user orsaved by the Client Computer.

Preferably, the vote associated with a particular content and thephysical distance between a content-providing Client Computer and acontent-receiving Client Computer will be used in determining whether topresent the content to the content-receiving Client Computer's user.Accordingly, if the distance from the content-providing Client Computerto the content-receiving Client Computer is further than the rank-weightof a content, the content may be dropped and not added to thecontent-receiving Client Computer's content.

As illustrated in FIG. 4A, content provided by distant users may not beas relevant as content of proximal users. The proximity-weightingattribute of the present invention (discussed below) permits ClientComputers that are positionally located in proximity to one another toexchange and share content, thereby providing a more relevantpresentation of content (FIG. 4B).

Any user, however, may use keywords (i.e., query-weighting), such thatcontent relevant to such topics will receive a higher ranking and beretained by the recipient's Client Computer. For example, if a User Aprovides content “apple” having a vote of +10 to Users B and C located10 km away, the content may be dropped by the Client Computer of User B(and thus not be presented to User B); User B's Client Computer's stackof content will be adjusted accordingly to present content of higherranking. However, if User C has set a query-weighting for “apple,” thenthe content will not be dropped by the Client Computer of User C (andthus will be presented to User C). Thus, the weighting that determineswhether received content will be presented/provided to other ClientComputers (or, alternatively, dropped) preferably depends upon therelative locations of the providing and content-receiving ClientComputers and the union of vote data of that content, as calculated bythe content-receiving Client Computer, taking into account anyuser-provided topic-weighting or query-weighting instructions. Theability of a particular content to be shared among all members of anetwork depends upon the ranking of such content, the distance betweenClient Computers, and the ranking of other content that may be providedto a recipient Client Computer. Thus, whereas the computer systems ofthe present invention have the ability to distribute a particularcontent to all Client Computers of the network, the systems (and/orindividual Client Computers thereof) also possess the ability to limitthe distribution of content in accordance with user-selected orsystem-determined weighting option(s).

B. Operation of a Preferred Non-Distributed Computer Systems of thePresent Invention

The computer systems of the present invention may be established as anon-distributed computer system, such as a centralized or regionalizedcomputer system. In such an embodiment, individual Client Computersprovide a central or regional server with their respective useridentifier coding, Client Computer identifier coding, positionallocation and weighting preferences. Content, and any votes relating tosuch content, are provided to the server, which then establishes acentralized database capable of being accessed by individual ClientComputers and of presenting to such Client Computers received contentthat has been weighted according to each such Client Computer'srespective proximity and other weighting preferences.

The use of a non-distributed computer system eliminates the possibilityof a data race, but entails interconnectivity (and preferably, real-timeinterconnectivity) between individual Client Computers and a remote, andpossibly distant, server.

IV. Open Computer Networks and Restricted Computer Networks

The distributed or non-distributed networks of the present invention maybe configured as a single Open Computer Network or as comprising one,two, three or more Restricted Computer Network(s). The computer networkof the present invention may alternatively be configured to compriseboth a single Open Computer Network and also such one, two, three ormore Restricted Computer Network(s).

As used herein, an “Open Computer Network” is one that any ClientComputer in communication range will be eligible to join, eitherautomatically, or more preferably, upon the request of the enteringClient Computer and subject to its user's agreement to General Terms OfService. As used herein, terms of service are said to be “General TermsOf Service” if they are not specific to particular users but arerequired of all users within communication range. Such General Terms OfService could include, for examples, restrictions on the provision ofcopyrighted materials, agreement to permit other Client Computers tocopy received content and provide such content to additional ClientComputers, restrictions on use, restrictions on the nature of contentthat may be provided, etc.)

In contrast, a “Restricted Computer Network” is one in which any ClientComputer in communication range will be eligible to join, eitherautomatically, or more preferably, upon the request of the enteringClient Computer, subject to agreement to Restricted Computer NetworkTerms Of Service. As used herein, terms of service are said to be“Restricted Computer Network Terms Of Service” if they are specific to aparticular Restricted Computer Network. For example, such RestrictedComputer Network Terms Of Service may require confidentiality or requirethe provision of authentication credential(s) (e.g., a password(textual, image, tonal, digital key, etc.) or biometric credential (suchas a fingerprint, face recognition match, DNA match, palm print, handgeometry match, iris recognition match, retinal pattern match,odor/scent match, typing rhythm, gait, voice pitch/accent, voicerecognition pattern, etc.) that is selected by and associated with suchRestricted Computer Network.

Preferably, although only those Client Computers that have beenauthenticated and accepted to a Restricted Computer Network will be ableto receive content provided by other Client Computers of the RestrictedComputer Network, all Client Computers within communication range ofsuch an accepted Client Computer will be able to provide content toClient Computers of the Restricted Computer Network. Thus, bothauthenticated and non-authenticated Client Computers assist indisseminating Restricted Computer Network content among theauthenticated Client Computers of the Restricted Computer Network, eventhough such content would not be perceivable by the users of thenon-authenticated Client Computers. Less preferably, only RestrictedComputer Network authenticated Client Computers will be able to providesuch Restricted Computer Network content to other authenticated ClientComputers.

Client computers of a Restricted Computer Network may provide andreceive “encrypted” content, which may then be decrypted by other ClientComputers of the Restricted Computer Network. For example, content maybe encrypted using an AES (128, 192, or 256 bit), a Triple DES (2-key or3-key), a CASTS (80 or 128 bit) encryption algorithm, etc. and anassociated encryption key (e.g., an RSA key, a Diffie-Hellman key, anMQV key, a key produced through an Elliptic Curve algorithm, etc.),which is possessed by Client Computers of such Restricted ComputerNetwork. Alternatively, a Restricted Computer Network may provide andreceive non-encrypted content.

When the computer system of the present invention comprises more thanone network, the selection of which network is to be used by acontent-providing Client Computer may be established by the ClientComputer's user, or may be established by the General Terms Of Service(for an Open Computer Network) or the such Restricted Computer NetworkTerms Of Service (for one or more Restricted Computer Networks).Alternatively, network selection may be determined heuristically basedupon a user's prior network selection(s). Alternatively, contentreceived from a particular joined network will automatically configurethe receiving Client Computer to provide responding content using thesame network, whether open or restricted, unless changed by the ClientComputer's user.

Preferably, however, a Client Computer that has joined an Open ComputerNetwork and one or more of such additional Restricted Computer Networkswill provide its user with the option of providing content on any ofsuch joined networks via, for example via the use of a “NetworkSelection Signal” (NSS). The Network Selection Signal may be an actualbutton, switch, slide, etc., or may be a screen icon of such a button,switch, slide, etc., or other signal or indication recognized by theClient Computer. The Network Selection Signal may alternatively beconfigured as a Client Computer orientation, a Client Computer movement(e.g., a “shaking” movement), a voice command, a sound, light or animage recognition etc.). Preferably the employed Network SelectionSignal will allow users to easily, rapidly and assuredly select adesired network for providing content to others. Preferably, the ClientComputer will provide its user with a feedback signal or otherindication indicating which network is to be employed for contentprovision.

Whereas the provision of the Open Computer Networks of the presentinvention has particular utility in social media and proximity-basedmass communication, the provision of the Restricted Computer Networks ofthe present invention, particularly if configured as a distributedcomputer network, has particular utility in enhancing the coordinationand security of police, firefighters and other emergency responders. Forexample, a Restricted Computer Network that is available to firstresponders at an emergency site allows police, firefighters and otheremergency personnel to communicate with one another without beingtethered to a centralized server. Instead, network content would “hop”from one responder's Client Computer to another responder's ClientComputer. Thus, the effective communication range would extend from theon-site command and control center to the most distant of the responders(see, FIG. 1). Moreover, since access to the content is restricted toauthenticated Client Computers, information communicated betweenresponders would be secure and confidential. Additionally, because thepreferred computer network will also comprise an Open Computer Network,first responders will be able to communicate with civilians and victims,separately from any communications with other first responders. Anexemplary set of communications among the first responders relating to afire scene is shown below in Table 1. In Table 1, the firefighters haveClient Computers that are joined to the open (“O”) network (therebyenabling their users to communicate with victims and non-emergencyresponse personnel whose Client Computers are also joined to the open(“O”) network. Additionally, the firefighters are joined to a restricted“Fire Responders” (“FR”) network. The Restricted Computer Network TermsOf Service of the FR network defaults provided content to the FR networkand require firefighters to provide a Network Selection Signal (“NSS”)in order to provide content to another network. Thus, as shown in Table1, firefighters provide an NSS of “O” in order to provide content overthe Open Computer Network. The general network terms of service of theopen (O) network presumes that content is to be provided over the OpenComputer Network and thus does not require any NSS signaling of thenetwork to be employed. The content shown in Table 1 is shown as textualfor ease of representation, but may be audio, image, video, etc., asdiscussed above.

TABLE 1 Content Content Msg Ntwk Provider NSS Provided Content Receivers1 R Fire Team 1 None “Fire is spreading to the third All first Neededfloor! Bring more hoses!” responders, but no others 2 O Fire Team 1 ◯“Is anyone trapped in here?” All first responders, and all others 3 OVictim 1 None “Help me! I'm John. I'm on All first Needed the thirdfloor!” responders, and all others 4 R Command None “Fire Team 1, we aresending All first Center Needed additional personnel to help responders,but John. Stay where you are and no others fight the fire.” 5 R FireTeam 1 None “Acknowledged; we will fight All first Needed the fire fromhere.” responders, but no others 6 O Fire Team 1 ◯ “John, stay where youare. All first Help is on the way.” responders, and all others 7 OVictim 1 None “OK” Needed 8 R Fire Team 2 None “We have John. He isinjured. All first Needed We will evacuate him. Have an responders, butambulance standing by!” no others Msg denotes Message Number. Ntwkdenotes Network. R denotes content provided/received on a RestrictedComputer Network. O denotes content provided/received on an OpenComputer Network.

Significantly, due to the ability of the individual Client Computers ofthe first responders to both receive and provide content, Message 1 isrelayed from Client Computer to Client Computer and is received by theCommand Center at substantially the same time as it is provided by FireTeam 1. Additionally, Message 3 is received from Victim 1 at the CommandCenter automatically, and at substantially the same time as it isreceived by Fire Team 1. Thus, information regarding Victim 1 isreceived at the Command Center without any active intervention by FireTeam 1. The decision to delay the rescue of Victim 1 until Fire Team 2is in position (Message 4) may reflect the existence of other victims orextenuating circumstances having higher priority. In order to preventpanic and to allow the firefighters to more effectively triage theemergency response, Message 4 is therefore provided over the RestrictedComputer Network and is thus not perceivable by Victim 1. All firstresponders simultaneously learn of the successful rescue of Victim 1through Message 7.

The use of the Restricted Computer Network of the present invention,particularly if configured as a distributed computer network, likewisehas particular utility in enhancing the coordination and security ofsoldiers and other military personnel engaged in military operations.The distributed configuration of the network permits its range to extendfrom the most advanced soldier to that soldier's command and controlcenter. Content provided to the Restricted Computer Network isimmediately provided to all Client Computers that have joined theRestricted Computer Network, thus facilitating communication betweensoldiers and soldiers and their command centers. As discussed above, thepresent invention contemplates computer networks that comprise multipleRestricted Computer Networks. Thus, it is possible to share informationwith only a subset of a force (e.g., officers, advance troops, corpsmen,supply teams, intra-squad, between nearby squads, global watchers,etc.).

The use of the Restricted Computer Network of the present invention,particularly if configured as a distributed computer network, similarlyhas particular utility in enhancing the coordination and security ofprivate businesses. The distributed configuration of the network permitsits range to extend across the entire facility of a business. Contentprovided to the Restricted Computer Network is immediately provided toall Client Computers have joined the Restricted Computer Network, thusfacilitating communication between individuals having sharedresponsibilities. As discussed above, the present invention contemplatescomputer networks that comprise multiple Restricted Computer Networks.Thus, it is possible to share information with only a subset of abusiness' personnel (e.g., management, shipping, sales, security, etc.).

V. Content Weighting and the Presentation of Weighted Content

As discussed above, the distributed or non-distributed networks of thepresent invention serve to disseminate content among Client Computersthat are joined to such networks. One aspect of the present inventionrelates to the ability of the Client Computers to restrict and/or filterreceived content in order to present to its user content that isweighted according to the user's preferences. Thus, for example, theClient Computer may be directed to present content that isproximity-weighted, rank-weighted, topic-weighted, query-weighted,time-weighted, location-weighted, locality-weighted, vote-weighted,and/or segment-weighted. Any combination of such weighting preferencesmay be employed.

As used herein, the term “proximity-weighted” is intended to denote thata Client Computer is to “weight” (i.e., filter, order, array, etc.) thecontent that is to be presented to such Client Computer's user so thatit is ranked according to the proximity of the content-providing ClientComputer to the locational position of the content-receiving ClientComputer. Thus, for example, a Client Computer applyingproximity-weighting to received content would present its user withcontent about topics being discussed within 5 meters of thecontent-receiving Client Computer's location, within 10 meters of thecontent-receiving Client Computer's location, within 25 meters of thecontent-receiving Client Computer's location, within 50 meters of thecontent-receiving Client Computer's location, within 100 meters of thecontent-receiving Client Computer's location, within 250 meters of thecontent-receiving Client Computer's location, within 500 meters of thecontent-receiving Client Computer's location, within 1 kilometer of thecontent-receiving Client Computer's location, within 5 kilometers of thecontent-receiving Client Computer's location, within 10 kilometers ofthe content-receiving Client Computer's location, within 25 kilometersof the content-receiving Client Computer's location, within 50kilometers of the content-receiving Client Computer's location, within100 kilometers of the content-receiving Client Computer's location, orwithin a larger distance from the content-receiving Client Computer'slocation. A user of such proximity-weighting would then be able toperceive the most frequently commented topics within his/her vicinity.

As used herein, the term “rank-weighted” is intended to denote that aClient Computer is to weight the content that is to be presented to suchClient Computer's user so that it is ranked according to the amount ofrelated content (relative to all content) received by the ClientComputer. Thus, for example, a Client Computer applying rank-weightingto received content would present its user with content pertaining tothe 1, 5, 10, 20, 50, 100, etc. most discussed topics received by theClient Computer. A user of such rank-weighting would then be able toperceive the most frequently commented topics provided to the ClientComputer.

As used herein, the term “topic-weighted” is intended to denote that aClient Computer is to weight the content that is to be presented to suchClient Computer's user so that it is ranked according to content “topic”(e.g., a name, event, subject, person, occurrence, etc.). Thus, forexample, a Client Computer applying topic-weighting to received contentwould present its user with related content for an alphabetically sortedlist of topics received by the Client Computer. A user of suchtopic-weighting would then be able to perceive content relevant to oneor more particular topics of interest.

As used herein, the term “query-weighted” is intended to denote that aClient Computer is to weight the content that is to be presented to suchClient Computer's user so that it is ranked according to its perceivedrelevance to search queries (keywords, images (which may beuser-perceivable, e.g., a photograph, etc., or may be machine-readable,e.g., a barcode, data matrix, Aztec code, QR code, etc.), video, sounds(e.g., a recorded voice, sound, music, etc.) provided by the user. Thus,for example, a Client Computer applying a query-weighting to receivedcontent would present its user with content pertaining to a particularinterest of the user (e.g., a name, event, subject, person, occurrence,etc.). A user of such query-weighting would then be able to perceivecontent relevant to particular topics satisfying the query. A singlequery or multiple queries may be simultaneously applied. Multiplequeries may be applied disjunctively (e.g., “weather” or “Seattle”) orconjunctively (e.g., “weather” and “Seattle”). Queries may employBoolean connectors (e.g., and, not, or, near, when, where, etc.). Byquerying an image or sound (e.g., a photograph of a particular child, orthe sound of that child's voice), a user would be able to useinterconnected Client Computers to locate the image or sound (e.g., tofind the child) if present or perceivable on the network. Similarly, byquerying for content in a particular user-selected language (e.g.,wherein the language of a text, sound, or video, etc. or the language oftext presented in an image, etc., is a particular user-selectedlanguage), a user would be able to use interconnected Client Computersto locate content that had been provided in such language. Thus, forexample, a user could access content relating to “weather” in “Seattle”in “German.” Thus, whereas topic-weighting presents a user with a listof topics received by the user's Client Computer, query-weightingpresents a user with a list of topics of particular relevance to suchuser.

As used herein, the term “time-weighted” is intended to denote that aClient Computer is to weight the content that is to be presented to suchClient Computer's user so that it is ranked according to its recency.Thus, for example, a Client Computer applying time-weighting to receivedcontent would present its user with related content for a list of the 1,5, 10, 20, 50, 100, etc. most recent topics provided to the ClientComputer, or topics arising within the most recent 10 minutes, 30minutes, hour, day, week, month, year, etc. A user of suchtime-weighting would then be able to perceive the most recent contentpresented to the Client Computer.

As used herein, the term “location-weighted” is intended to denote thata Client Computer is to weight the content that is to be presented tosuch Client Computer's user so that it is ranked according to auser-selected place or location. Thus, for example, a Client Computerapplying a location-weighting to received content would present its userwith content pertaining to a particular area, such as a college oruniversity campus, a neighborhood, an event locale (e.g., an amusementpark, a fair, a cruise ship, a conference, etc.), a city, a state, acountry, a sub-continental region (e.g., Northern Europe, the MiddleEast, etc.), or a continent. A user of such location-weighting wouldthen be able to perceive content involving such location.

As used herein, the term “locality-weighted” is intended to weight thecontent that is to be presented to such Client Computer's user so thatit is ranked according to a user's home region. Thus, for example, aClient Computer applying a location-weighting to received content wouldpresent its user with content pertaining to a user's hometown, county,state, etc. irrespective of the user's current location.

As used herein, the term “vote-weighted” is intended to denote that aClient Computer is to weight the content that is to be presented to suchClient Computer's user so that it is ranked according to the percentageof favorable votes received (relative to all received votes) that suchcontent has received from other users whose Client Computers havepreviously received such content and whose users have voted theirresponses or reactions to such content. Thus, for example, a ClientComputer applying vote-weighting to received content would present itsuser with content that had received a vote of greater than 50% favorablevotes, greater than 60% favorable votes, greater than 70% favorablevotes, greater than 80% favorable votes, greater than 90% favorablevotes, greater than 95% favorable votes, etc. A user of suchvote-weighting would then be able to perceive the most liked commentedtopics provided to the Client Computer.

As used herein, the term “segment-weighted” is intended to denote that aClient Computer (or, as discussed below, a Content-Monitoring Computerof, or accessing, the network) is to weight content that is to bepresented to such computer's user so that it is ranked according to oneor more of the network-set segment criteria (e.g., age of other users,income of other users, timedate (day, date or time in which other usersaccessed the content), location of other users when accessing thecontent, locality (home region) of other users, identification (gender,ethnicity, race, locality, language, etc.) of other users, etc. Thepresent invention envisions the use of these, and/or any other, desiredsegmenting criteria in any combination or permutation. Thus, toillustrate, the client or other network-accessing computer could assessand present content of interest to young girls, retired male seniorcitizens, French-speaking women, exotic car owners, etc.Segment-weighting is thus similar to query-weighting, but differs inthat the segments available for segment-weighting are established by thenetwork, whereas the queries available for query-weighting areestablished individually by the Client Computers.

Each of such weighting preferences may be applied solely, so as topresent a user with content that is only proximity-weighted, onlyrank-weighted, only topic-weighted, only query-weighted, onlytime-weighted, only location-weighted, only locality-weighted, onlyvote-weighted, or only segment-weighted (for example to provide a ClientComputer with the most proximal 1, 2, 5, 10, 20, 50, 100, etc. topics,irrespective of their ranking, topic, recency or location). Morepreferably, at the request of a user, a Client Computer may be directedto simultaneously apply any 2, any 3, any 4, any 5, any 6, any 7, any 8,or 9 or more weighting preferences, so as to present the user withrelated content:

-   A. that has been simultaneously weighting with 2 weighting    preferences, such as: proximity-weighting and rank-weighting; or    proximity-weighting and topic-weighting; or proximity-weighting and    query-weighting; or proximity-weighting and time-weighting; or    proximity-weighting and location-weighting; or proximity-weighting    and locality-weighting; or proximity-weighting and vote-weighting;    or proximity-weighting and segment-weighting; or rank-weighting and    topic-weighting; or rank-weighting and query-weighting; or    rank-weighting and time-weighting; or rank-weighting and    location-weighting; or rank-weighting and locality-weighting; or    rank-weighting and vote-weighting; or rank-weighting and    segment-weighting; or topic-weighting and query-weighting; or    topic-weighting and time-weighting; or topic-weighting and    location-weighting; or topic-weighting and locality-weighting; or    topic-weighting and vote-weighting; or topic-weighting and    segment-weighting; or query-weighting and time-weighting; or    query-weighting and location-weighting; or query-weighting and    locality-weighting; or query-weighting and vote-weighting; or    query-weighting and segment-weighting; or time-weighting and    location-weighting; or time-weighting and locality-weighting; or    time-weighting and vote-weighting; or time-weighting and    segment-weighting; or location-weighting and locality-weighting; or    location-weighting and vote-weighting; or location-weighting and    segment-weighting; or locality-weighting and vote-weighting; or    locality-weighting and segment-weighting; or vote-weighting and    segment-weighting; or-   B. that simultaneously applies 3 weighting preferences, such as:    proximity-weighting and rank-weighting and topic-weighting; or    proximity-weighting and rank-weighting and query-weighting; or    proximity-weighting and rank-weighting and time-weighting; or    proximity-weighting and rank-weighting and location-weighting; or    proximity-weighting and rank-weighting and locality-weighting; or    proximity-weighting and rank-weighting and vote-weighting; or    proximity-weighting and rank-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting; or    proximity-weighting and topic-weighting and time-weighting; or    proximity-weighting and topic-weighting and location-weighting; or    proximity-weighting and topic-weighting and locality-weighting; or    proximity-weighting and topic-weighting and vote-weighting; or    proximity-weighting and topic-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting; or    proximity-weighting and query-weighting and location-weighting; or    proximity-weighting and query-weighting and locality-weighting; or    proximity-weighting and query-weighting and vote-weighting; or    proximity-weighting and query-weighting and segment-weighting; or    proximity-weighting and time-weighting and location-weighting; or    proximity-weighting and time-weighting and locality-weighting; or    proximity-weighting and time-weighting and vote-weighting; or    proximity-weighting and time-weighting and segment-weighting; or    proximity-weighting and location-weighting and locality-weighting;    or proximity-weighting and location-weighting and vote-weighting; or    proximity-weighting and location-weighting and segment-weighting; or    proximity-weighting and locality-weighting and vote-weighting; or    proximity-weighting and locality-weighting and segment-weighting; or    proximity-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting; or    rank-weighting and topic-weighting and time-weighting; or    rank-weighting and topic-weighting and location-weighting; or    rank-weighting and topic-weighting and locality-weighting; or    rank-weighting and topic-weighting and vote-weighting; or    rank-weighting and topic-weighting and segment-weighting; or    rank-weighting and query-weighting and time-weighting; or    rank-weighting and query-weighting and location-weighting; or    rank-weighting and query-weighting and locality-weighting; or    rank-weighting and query-weighting and vote-weighting; or    rank-weighting and query-weighting and segment-weighting; or    rank-weighting and time-weighting and location-weighting; or    rank-weighting and time-weighting and locality-weighting; or    rank-weighting and time-weighting and vote-weighting; or    rank-weighting and time-weighting and segment-weighting; or    rank-weighting and location-weighting and locality-weighting; or    rank-weighting and location-weighting and vote-weighting; or    rank-weighting and location-weighting and segment-weighting; or    rank-weighting and locality-weighting and vote-weighting; or    rank-weighting and locality-weighting and segment-weighting; or    rank-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and time-weighting; or    topic-weighting and query-weighting and location-weighting; or    topic-weighting and query-weighting and locality-weighting; or    topic-weighting and query-weighting and vote-weighting; or    topic-weighting and query-weighting and segment-weighting; or    topic-weighting and time-weighting and location-weighting; or    topic-weighting and time-weighting and locality-weighting; or    topic-weighting and time-weighting and vote-weighting; or    topic-weighting and time-weighting and segment-weighting; or    topic-weighting and location-weighting and locality-weighting; or    topic-weighting and location-weighting and vote-weighting; or    topic-weighting and location-weighting and segment-weighting; or    topic-weighting and locality-weighting and vote-weighting; or    topic-weighting and locality-weighting and segment-weighting; or    topic-weighting and vote-weighting and segment-weighting; or    query-weighting and time-weighting and location-weighting; or    query-weighting and time-weighting and locality-weighting; or    query-weighting and time-weighting and vote-weighting; or    query-weighting and time-weighting and segment-weighting; or    query-weighting and location-weighting and locality-weighting; or    query-weighting and location-weighting and vote-weighting; or    query-weighting and location-weighting and segment-weighting; or    query-weighting and locality-weighting and vote-weighting; or    query-weighting and locality-weighting and segment-weighting; or    query-weighting and vote-weighting and segment-weighting; or    time-weighting and location-weighting and locality-weighting; or    time-weighting and location-weighting and vote-weighting; or    time-weighting and location-weighting and segment-weighting; or    time-weighting and locality-weighting and vote-weighting; or    time-weighting and locality-weighting and segment-weighting; or    time-weighting and vote-weighting and segment-weighting; or    location-weighting and locality-weighting and vote-weighting; or    location-weighting and locality-weighting and segment-weighting; or    location-weighting and vote-weighting and segment-weighting; or    locality-weighting and vote-weighting and segment-weighting; or-   C. that simultaneously applies 4 weighting preferences, such as:    proximity-weighting and rank-weighting and topic-weighting and    query-weighting; or proximity-weighting and rank-weighting and    topic-weighting and time-weighting; or proximity-weighting and    rank-weighting and topic-weighting and location-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    topic-weighting and vote-weighting; or proximity-weighting and    rank-weighting and topic-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting; or proximity-weighting and rank-weighting and    query-weighting and location-weighting; or proximity-weighting and    rank-weighting and query-weighting and locality-weighting; or    proximity-weighting and rank-weighting and query-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    query-weighting and segment-weighting; or proximity-weighting and    rank-weighting and time-weighting and location-weighting; or    proximity-weighting and rank-weighting and time-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    time-weighting and vote-weighting; or proximity-weighting and    rank-weighting and time-weighting and segment-weighting; or    proximity-weighting and rank-weighting and location-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    location-weighting and vote-weighting; or proximity-weighting and    rank-weighting and location-weighting and segment-weighting; or    proximity-weighting and rank-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    rank-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting; or proximity-weighting and topic-weighting and    query-weighting and location-weighting; or proximity-weighting and    topic-weighting and query-weighting and locality-weighting; or    proximity-weighting and topic-weighting and query-weighting and    vote-weighting; or proximity-weighting and topic-weighting and    query-weighting and segment-weighting; or proximity-weighting and    topic-weighting and time-weighting and location-weighting; or    proximity-weighting and topic-weighting and time-weighting and    locality-weighting; or proximity-weighting and topic-weighting and    time-weighting and vote-weighting; or proximity-weighting and    topic-weighting and time-weighting and segment-weighting; or    proximity-weighting and topic-weighting and location-weighting and    locality-weighting; or proximity-weighting and topic-weighting and    location-weighting and vote-weighting; or proximity-weighting and    topic-weighting and location-weighting and segment-weighting; or    proximity-weighting and topic-weighting and locality-weighting and    vote-weighting; or proximity-weighting and topic-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    topic-weighting and vote-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting and    location-weighting; or proximity-weighting and query-weighting and    time-weighting and locality-weighting; or proximity-weighting and    query-weighting and time-weighting and vote-weighting; or    proximity-weighting and query-weighting and time-weighting and    segment-weighting; or proximity-weighting and query-weighting and    location-weighting and locality-weighting; or proximity-weighting    and query-weighting and location-weighting and vote-weighting; or    proximity-weighting and query-weighting and location-weighting and    segment-weighting; or proximity-weighting and query-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    query-weighting and locality-weighting and segment-weighting; or    proximity-weighting and query-weighting and vote-weighting and    segment-weighting; or proximity-weighting and time-weighting and    location-weighting and locality-weighting; or proximity-weighting    and time-weighting and location-weighting and vote-weighting; or    proximity-weighting and time-weighting and location-weighting and    segment-weighting; or proximity-weighting and time-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    time-weighting and locality-weighting and segment-weighting; or    proximity-weighting and time-weighting and vote-weighting and    segment-weighting; or proximity-weighting and location-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and location-weighting and vote-weighting and    segment-weighting; or proximity-weighting and locality-weighting and    vote-weighting and segment-weighting; or rank-weighting and    topic-weighting and query-weighting and time-weighting; or    rank-weighting and topic-weighting and query-weighting and    location-weighting; or rank-weighting and topic-weighting and    query-weighting and locality-weighting; or rank-weighting and    topic-weighting and query-weighting and vote-weighting; or    rank-weighting and topic-weighting and query-weighting and    segment-weighting; or rank-weighting and topic-weighting and    time-weighting and location-weighting; or rank-weighting and    topic-weighting and time-weighting and locality-weighting; or    rank-weighting and topic-weighting and time-weighting and    vote-weighting; or rank-weighting and topic-weighting and    time-weighting and segment-weighting; or rank-weighting and    topic-weighting and location-weighting and locality-weighting; or    rank-weighting and topic-weighting and location-weighting and    vote-weighting; or rank-weighting and topic-weighting and    location-weighting and segment-weighting; or rank-weighting and    topic-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and locality-weighting and    segment-weighting; or rank-weighting and topic-weighting and    vote-weighting and segment-weighting; or rank-weighting and    query-weighting and time-weighting and location-weighting; or    rank-weighting and query-weighting and time-weighting and    locality-weighting; or rank-weighting and query-weighting and    time-weighting and vote-weighting; or rank-weighting and    query-weighting and time-weighting and segment-weighting; or    rank-weighting and query-weighting and location-weighting and    locality-weighting; or rank-weighting and query-weighting and    location-weighting and vote-weighting; or rank-weighting and    query-weighting and location-weighting and segment-weighting; or    rank-weighting and query-weighting and locality-weighting and    vote-weighting; or rank-weighting and query-weighting and    locality-weighting and segment-weighting; or rank-weighting and    query-weighting and vote-weighting and segment-weighting; or    rank-weighting and time-weighting and location-weighting and    locality-weighting; or rank-weighting and time-weighting and    location-weighting and vote-weighting; or rank-weighting and    time-weighting and location-weighting and segment-weighting; or    rank-weighting and time-weighting and locality-weighting and    vote-weighting; or rank-weighting and time-weighting and    locality-weighting and segment-weighting; or rank-weighting and    time-weighting and vote-weighting and segment-weighting; or    rank-weighting and location-weighting and locality-weighting and    vote-weighting; or rank-weighting and location-weighting and    locality-weighting and segment-weighting; or rank-weighting and    location-weighting and vote-weighting and segment-weighting; or    rank-weighting and locality-weighting and vote-weighting and    segment-weighting; or topic-weighting and query-weighting and    time-weighting and location-weighting; or topic-weighting and    query-weighting and time-weighting and locality-weighting; or    topic-weighting and query-weighting and time-weighting and    vote-weighting; or topic-weighting and query-weighting and    time-weighting and segment-weighting; or topic-weighting and    query-weighting and location-weighting and locality-weighting; or    topic-weighting and query-weighting and location-weighting and    vote-weighting; or topic-weighting and query-weighting and    location-weighting and segment-weighting; or topic-weighting and    query-weighting and locality-weighting and vote-weighting; or    topic-weighting and query-weighting and locality-weighting and    segment-weighting; or topic-weighting and query-weighting and    vote-weighting and segment-weighting; or topic-weighting and    time-weighting and location-weighting and locality-weighting; or    topic-weighting and time-weighting and location-weighting and    vote-weighting; or topic-weighting and time-weighting and    location-weighting and segment-weighting; or topic-weighting and    time-weighting and locality-weighting and vote-weighting; or    topic-weighting and time-weighting and locality-weighting and    segment-weighting; or topic-weighting and time-weighting and    vote-weighting and segment-weighting; or topic-weighting and    location-weighting and locality-weighting and vote-weighting; or    topic-weighting and location-weighting and locality-weighting and    segment-weighting; or topic-weighting and location-weighting and    vote-weighting and segment-weighting; or topic-weighting and    locality-weighting and vote-weighting and segment-weighting; or    query-weighting and time-weighting and location-weighting and    locality-weighting; or query-weighting and time-weighting and    location-weighting and vote-weighting; or query-weighting and    time-weighting and location-weighting and segment-weighting; or    query-weighting and time-weighting and locality-weighting and    vote-weighting; or query-weighting and time-weighting and    locality-weighting and segment-weighting; or query-weighting and    time-weighting and vote-weighting and segment-weighting; or    query-weighting and location-weighting and locality-weighting and    vote-weighting; or query-weighting and location-weighting and    locality-weighting and segment-weighting; or query-weighting and    location-weighting and vote-weighting and segment-weighting; or    query-weighting and locality-weighting and vote-weighting and    segment-weighting; or time-weighting and location-weighting and    locality-weighting and vote-weighting; or time-weighting and    location-weighting and locality-weighting and segment-weighting; or    time-weighting and location-weighting and vote-weighting and    segment-weighting; or time-weighting and locality-weighting and    vote-weighting and segment-weighting; or location-weighting and    locality-weighting and vote-weighting and segment-weighting; or-   D. that simultaneously applies 5 weighting preferences, such as:    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    location-weighting; or proximity-weighting and rank-weighting and    topic-weighting and query-weighting and locality-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and vote-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    topic-weighting and time-weighting and location-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and locality-weighting; or proximity-weighting and    rank-weighting and topic-weighting and time-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    topic-weighting and time-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    location-weighting and locality-weighting; or proximity-weighting    and rank-weighting and topic-weighting and location-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    topic-weighting and location-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    rank-weighting and topic-weighting and locality-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    topic-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and location-weighting; or proximity-weighting and    rank-weighting and query-weighting and time-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    query-weighting and time-weighting and vote-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and segment-weighting; or proximity-weighting and    rank-weighting and query-weighting and location-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    query-weighting and location-weighting and vote-weighting; or    proximity-weighting and rank-weighting and query-weighting and    location-weighting and segment-weighting; or proximity-weighting and    rank-weighting and query-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    query-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    rank-weighting and time-weighting and location-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    time-weighting and location-weighting and vote-weighting; or    proximity-weighting and rank-weighting and time-weighting and    location-weighting and segment-weighting; or proximity-weighting and    rank-weighting and time-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    time-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and time-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    rank-weighting and location-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and location-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    rank-weighting and locality-weighting and vote-weighting and    segment-weighting; or proximity-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and locality-weighting; or proximity-weighting and    topic-weighting and query-weighting and time-weighting and    vote-weighting; or proximity-weighting and topic-weighting and    query-weighting and time-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    location-weighting and locality-weighting; or proximity-weighting    and topic-weighting and query-weighting and location-weighting and    vote-weighting; or proximity-weighting and topic-weighting and    query-weighting and location-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    topic-weighting and query-weighting and locality-weighting and    segment-weighting; or proximity-weighting and topic-weighting and    query-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting; or proximity-weighting    and topic-weighting and time-weighting and location-weighting and    vote-weighting; or proximity-weighting and topic-weighting and    time-weighting and location-weighting and segment-weighting; or    proximity-weighting and topic-weighting and time-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    topic-weighting and time-weighting and locality-weighting and    segment-weighting; or proximity-weighting and topic-weighting and    time-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and location-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    topic-weighting and location-weighting and locality-weighting and    segment-weighting; or proximity-weighting and topic-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and locality-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting; or proximity-weighting and query-weighting and    time-weighting and location-weighting and vote-weighting; or    proximity-weighting and query-weighting and time-weighting and    location-weighting and segment-weighting; or proximity-weighting and    query-weighting and time-weighting and locality-weighting and    vote-weighting; or proximity-weighting and query-weighting and    time-weighting and locality-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    query-weighting and location-weighting and locality-weighting and    vote-weighting; or proximity-weighting and query-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and query-weighting and location-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    query-weighting and locality-weighting and vote-weighting and    segment-weighting; or proximity-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and time-weighting and location-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    time-weighting and location-weighting and vote-weighting and    segment-weighting; or proximity-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and location-weighting and locality-weighting    and vote-weighting and segment-weighting; or rank-weighting and    topic-weighting and query-weighting and time-weighting and    location-weighting; or rank-weighting and topic-weighting and    query-weighting and time-weighting and locality-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and vote-weighting; or rank-weighting and    topic-weighting and query-weighting and time-weighting and    segment-weighting; or rank-weighting and topic-weighting and    query-weighting and location-weighting and locality-weighting; or    rank-weighting and topic-weighting and query-weighting and    location-weighting and vote-weighting; or rank-weighting and    topic-weighting and query-weighting and location-weighting and    segment-weighting; or rank-weighting and topic-weighting and    query-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and query-weighting and    locality-weighting and segment-weighting; or rank-weighting and    topic-weighting and query-weighting and vote-weighting and    segment-weighting; or rank-weighting and topic-weighting and    time-weighting and location-weighting and locality-weighting; or    rank-weighting and topic-weighting and time-weighting and    location-weighting and vote-weighting; or rank-weighting and    topic-weighting and time-weighting and location-weighting and    segment-weighting; or rank-weighting and topic-weighting and    time-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and time-weighting and    locality-weighting and segment-weighting; or rank-weighting and    topic-weighting and time-weighting and vote-weighting and    segment-weighting; or rank-weighting and topic-weighting and    location-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and location-weighting and    locality-weighting and segment-weighting; or rank-weighting and    topic-weighting and location-weighting and vote-weighting and    segment-weighting; or rank-weighting and topic-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting; or rank-weighting and    query-weighting and time-weighting and location-weighting and    vote-weighting; or rank-weighting and query-weighting and    time-weighting and location-weighting and segment-weighting; or    rank-weighting and query-weighting and time-weighting and    locality-weighting and vote-weighting; or rank-weighting and    query-weighting and time-weighting and locality-weighting and    segment-weighting; or rank-weighting and query-weighting and    time-weighting and vote-weighting and segment-weighting; or    rank-weighting and query-weighting and location-weighting and    locality-weighting and vote-weighting; or rank-weighting and    query-weighting and location-weighting and locality-weighting and    segment-weighting; or rank-weighting and query-weighting and    location-weighting and vote-weighting and segment-weighting; or    rank-weighting and query-weighting and locality-weighting and    vote-weighting and segment-weighting; or rank-weighting and    time-weighting and location-weighting and locality-weighting and    vote-weighting; or rank-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    rank-weighting and time-weighting and location-weighting and    vote-weighting and segment-weighting; or rank-weighting and    time-weighting and locality-weighting and vote-weighting and    segment-weighting; or rank-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting; or topic-weighting and    query-weighting and time-weighting and location-weighting and    vote-weighting; or topic-weighting and query-weighting and    time-weighting and location-weighting and segment-weighting; or    topic-weighting and query-weighting and time-weighting and    locality-weighting and vote-weighting; or topic-weighting and    query-weighting and time-weighting and locality-weighting and    segment-weighting; or topic-weighting and query-weighting and    time-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and location-weighting and    locality-weighting and vote-weighting; or topic-weighting and    query-weighting and location-weighting and locality-weighting and    segment-weighting; or topic-weighting and query-weighting and    location-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and locality-weighting and    vote-weighting and segment-weighting; or topic-weighting and    time-weighting and location-weighting and locality-weighting and    vote-weighting; or topic-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    topic-weighting and time-weighting and location-weighting and    vote-weighting and segment-weighting; or topic-weighting and    time-weighting and locality-weighting and vote-weighting and    segment-weighting; or topic-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting; or query-weighting and    time-weighting and location-weighting and locality-weighting and    segment-weighting; or query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    query-weighting and time-weighting and locality-weighting and    vote-weighting and segment-weighting; or query-weighting and    location-weighting and locality-weighting and vote-weighting and    segment-weighting; or time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or-   E. that simultaneously applies 6 weighting preferences, such as:    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and locality-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and vote-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and location-weighting and locality-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and location-weighting and vote-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and location-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and locality-weighting and vote-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and location-weighting and locality-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and location-weighting and vote-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and location-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and locality-weighting and vote-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and location-weighting and vote-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and location-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and locality-weighting and vote-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and rank-weighting and query-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and rank-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and vote-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and locality-weighting and vote-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and locality-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and topic-weighting and query-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and topic-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    proximity-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and query-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and vote-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and locality-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    location-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and query-weighting and    location-weighting and locality-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    location-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    rank-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    rank-weighting and topic-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    rank-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    rank-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    rank-weighting and query-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and query-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting; or    topic-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    topic-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and time-weighting and    locality-weighting and vote-weighting and segment-weighting; or    topic-weighting and query-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    topic-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or-   F. that simultaneously applies 7 weighting preferences, such as:    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting; or proximity-weighting and rank-weighting and    topic-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    topic-weighting and query-weighting and time-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    time-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and location-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    topic-weighting and query-weighting and location-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and locality-weighting and vote-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    topic-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    rank-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    time-weighting and location-weighting and vote-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    topic-weighting and time-weighting and locality-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    rank-weighting and topic-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting and    vote-weighting; or proximity-weighting and rank-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    rank-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and locality-weighting and vote-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    query-weighting and location-weighting and locality-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    rank-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting and    vote-weighting; or proximity-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting and segment-weighting; or proximity-weighting and    topic-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and topic-weighting and query-weighting and    time-weighting and locality-weighting and vote-weighting and    segment-weighting; or proximity-weighting and topic-weighting and    query-weighting and location-weighting and locality-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    topic-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting and    segment-weighting; or rank-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting; or rank-weighting and    topic-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and segment-weighting; or    rank-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and vote-weighting and    segment-weighting; or rank-weighting and topic-weighting and    query-weighting and time-weighting and locality-weighting and    vote-weighting and segment-weighting; or rank-weighting and    topic-weighting and query-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    rank-weighting and topic-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting and    segment-weighting; or rank-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting and    vote-weighting and segment-weighting; or topic-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or-   G. that simultaneously applies 8 weighting preferences, such as:    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    topic-weighting and query-weighting and time-weighting and    location-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and topic-weighting and    query-weighting and time-weighting and locality-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    rank-weighting and topic-weighting and query-weighting and    location-weighting and locality-weighting and vote-weighting and    segment-weighting; or proximity-weighting and rank-weighting and    topic-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or    proximity-weighting and rank-weighting and query-weighting and    time-weighting and location-weighting and locality-weighting and    vote-weighting and segment-weighting; or proximity-weighting and    topic-weighting and query-weighting and time-weighting and    location-weighting and locality-weighting and vote-weighting and    segment-weighting; or rank-weighting and topic-weighting and    query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting; or-   H. that simultaneously applies 9 (or more) weighting preferences,    such as: proximity-weighting and rank-weighting and topic-weighting    and query-weighting and time-weighting and location-weighting and    locality-weighting and vote-weighting and segment-weighting.

By simultaneously applying multiple weighting preferences, a ClientComputer can, for example, present to its user related content of thetop 1, 2, 5 10, etc. topics being discussed in a room (currently or at aprior user-selected time), or the current (or prior) topics of interesttrending in a particular city, etc. Thus, for example, the inventionpermits attendees of a conference to share content related to topicsarising at the conference, or permits individuals interested in beingaware of news affecting a region area to be able to determine the mostimportant or latest topics pertaining to such region. The architectureand configuration of the computer system permits the top trending topicsto vary as attendees move around the conference, consistent with localvariation in the topic rankings. Similarly, individuals concerned abouta weather or traffic event may communicate with one another using thepresent invention.

The Client Computer will preferably permit its user to respond tocontent provided by other users, so as to permit a content“conversation.” The Client Computer will also preferably permit a userto register a vote as to whether such user approves, agrees, “likes,”etc. such content or disapproves, disagrees, or “dislikes” such content.Such voting facilitates the ability of a computer system of the presentinvention to establish rank-weighting rankings, time-weighting rankings,etc. The Client Computer will preferably permit a user to respond tosuch votes, on an individualized or global manner, so as to permit avoting user to view additional information (such as the user's name.username, etc.). Thus, for example, a user posting content anonymouslymay receive a vote of approval from another user, and respond bypermitting the approving user to learn the posting user's identity,location, etc. Preferably, such permission may be subsequently augmentedor terminated by the posting user.

As indicated above, in one embodiment of the present invention, a ClientComputer of the present invention will have the ability to discern oridentify the language of received content (e.g., the language used inreceived text, sound, image (e.g., advertisements, menus, notices, maps,street signs, etc.), video, etc.). Thus, such client computers will havethe ability to present content that was received in one or morelanguages. In a further embodiment, such Client Computers will also havethe ability to translate and present such received “first” languagecontent into one, two or more user-selected alternative (or additional)language(s) (collectively referred to as a “second” language), so as tobe able to receive content that was in such “first” language and presentit to that user in such “second” language. By adjusting a user'spreferences, such a Client Computer can present such content exclusivelyin such “first” language, exclusively in such “second” language, or inboth such “first” language and such “second” language (for example, inbilingual regions, such as Quebec, or trilingual regions such as India).Such first or second languages may, for example, be Chinese, Spanish,English, Hindi, Arabic, Portuguese, Bengali, Urdu, Russian, Japanese,Punjabi/Lahnda, French, German, Korean, etc. In one embodiment, suchtranslation and presentation will be accomplished upon the direction ofindividual users (through each such user's language user preference orweighting preferences), so as, for example, to allow a user to accesspresented local content in that user's native language. Thus, forexample, an advertisement in a first language would be translated into asecond language at a user's direction in order to permit such user toreview the content in the user's preferred language. In a secondembodiment, such translation and presentation will be accomplished uponthe direction of the network (subject to a user's language userpreference or weighting preferences), so as, for example, to allowcontent that was prepared in one language to be automatically andinitially translated into a second language and provided to such userswho are within a geographic area known to have a high level ofindividuals for whom such second language is their native language.Thus, for example, an advertisement in a first language would betranslated into differing second language(s) at the network's directionin order to permit such content to be more widely appreciated by usersin geographical areas that are associated with users of differingpreferred language(s).

The ability of a Client Computer to translate such content and presentsuch translations may be accomplished using, for example: an internallanguage translation program (that may use, for example, an internalstatistical machine translation technology (SMT) or a hybrid neuralmachine translation (NMT) technology), or a third-party translation ordictionary program (for example, Apertium, Babylon, Power Translator,Prompt, Personal Translator, Systran, IdiomaX, Cute Translator, WordMagic, NeuroTran, etc.), or by permitting a user's Client Computer toaccess a publicly available translation site or capability (for example,Babelfish.com®, Cucumis, Flitto, Google Translate, Khandbahale.com,Microsoft Translator, Omniscien Technologies, ProZ.com, UniLang,Yandex.Translate, Yeminli Sözlük, etc.). Access to, and use of, suchthird-party translation program or such publicly available translationsite or capability (which may be internet-based, network-based or storedon the Client Computer for offline use) may, for example, be licensed tothe Client Computer from the third party through payment of a shared ornon-shared user fee.

In a further embodiment, the Client Computers of the present inventionmay have the ability to discern or identify price and/or pricinginformation of received content (e.g., price and/or pricing informationrelating to goods or services in signage, advertisements, notices,menus, etc. of such received content) that is in a “first” currency andto convert and present such received “first” currency content into auser-selected alternative (or additional) “second” currency, so as to beable to receive content that was in such “first” currency and present itto that user in such “second” currency. By adjusting a user'spreferences, such a Client Computer can present such content exclusivelyin such “first” currency, exclusively in such “second” currency, or inboth such “first” currency and such “second” currency. Since therelative valuation of currencies varies, such ability of a ClientComputer to calculate and present such currency conversions ispreferably accomplished using a third-party currency calculation programor a publicly available currency calculation site or capability (forexample, XE.com, travelex.com, transferwise.com, ofx.com, etc.). Accessto, and use of, such third-party translation program or such publiclyavailable translation site or capability (which may be internet-based,network-based, or updated and stored on the Client Computer) may, forexample, be licensed to the Client Computer from the third party throughpayment of a shared or non-shared user fee.

V. Content-Monitoring Computers and Content Flow Analysis(“Data-Mining”)

In a preferred embodiment, one or more of the computers of a computersystem of the present invention will be configured as a“Content-Monitoring” computer. Content Monitoring computers differ fromthe above-described Client Computers, among other attributes, in beingparticularly adapted to analyze network content flow relating to one ormore particular topics selected by the Content-Monitoring Computer'suser or to network-selected segment(s). Content-Monitoring Computerspreferably do not automatically drop from their stacks content of lesservalue, but retain such content so that time-based variations of contentamount or frequency can be provided. Content-Monitoring Computers thushave the ability to monitor the flow of content across the network.Content-Monitoring Computers will preferably have greater processingpower and greater memory capacity than other Client Computers.Content-Monitoring Computers may be non-mobile devices, such as desktopcomputers, servers, etc.

As used herein, the term “Content-Selected Monitoring,” and variantsthereof, are intended to denote the ability of a Content-MonitoringComputer to discern Client Computers that are receiving or providingselected content. Preferably, such discernment will be anonymous to theextent that the personal identities (i.e., name, username, etc.) and/orattributes (address, age, credit card information, etc.) of the users ofsuch discerned Client Computers are not conveyed to, or are not storedby, the Content-Monitoring Computer. Significantly, theContent-Monitoring Computer will, however, preferably have the abilityto access the user's identifier coding (or the Client Computer'sidentifier coding). This attribute facilitates the ability ofContent-Monitoring Computers to categorize users and the favorablevoted-upon content or disseminated content of their Client Computersbased on the profile of the Client Computer's user (“user profile”).Whereas some aspects of a user's profile will be non-anonymous (e.g.,information required to establish and fund a user's account), otheraspects of a user's profile may be discerned by the Content-MonitoringComputer in an anonymous manner (i.e., without correlating such aspectsto name, address or other information that would permit others toidentify the user). Such anonymous aspects of a user's profile mayinclude the user's age, income level, location and identification(gender, ethnicity, race, locality, etc.). Thus, such Content-MonitoringComputers possess an ability to discern and analyze the union of topicsof interest that they access, and to track changes in the recency,magnitude, excitement, or other attribute(s) or characteristic(s) withwhich such users interact with one another or with particular products.Thus, the computer systems of the present invention may be operated toprovide user(s) of Content-Monitoring Computers with the ability toperceive the flow of content across the entire network or any sub-regionthereof. In combination with query-weighting content presentationrequests, such Content-Monitoring Computers can be used to evaluate theextent to which such selected flow relates to a user-selected attribute(i.e., facilitating “data-mining” of a current content flow). In furthercombination with a time-weighting content presentation request, and whena log or database of past content has been created, suchContent-Monitoring Computers allow their users to access content thatflowed across the network at specified time(s) in the past, or inparticular locations, or for specified time period duration(s), whichcan be used to evaluate the extent to which such selected past flowrelates to a user-selected attribute (i.e., “data-mining” a past contentflow).

Thus, for example, a Content-Monitoring Computer that has beeninstructed to monitor content relating to a particular service, event,location, product, etc. would be able to discern the percentage ofClient Computers on the network that have been instructed to receive orprovide content relating to such service, event, location, product, etc.Preferably, the Content-Monitoring Computer would also be capable ofaccessing such content so as to permit its user to perceive thefavorability/disfavorability, ranking, recency, topics etc. pertainingto such content. Thus, for example, a Content-Monitoring Computerinstructed to monitor content relevant to a selected product (e.g., afast food product, a television, etc.) or a selected event (e.g.,individuals entering an office building, attending a fair, or visiting amuseum) would be able to discern, typically anonymously, how many usersare commenting about the product or event (in absolute number, or inrelative to the total number of Client Computers of the network, and thereactions of such users to the selected product. A manufacturer,sponsor, administrator, etc. could then use such information to improveor vary the product or alter the event conditions (e.g., re-allocatesales help, address depleting inventory, address over-crowding, etc.) soas to increase the favorability, etc. of the product or event.

An additional attribute of the present invention is that suchContent-Selected Monitoring may be cross-correlated with other contentto provide the Content-Monitoring Computer with the ability to discernthe demographics of users whose Client Computers have been instructed toreceive or provide the selected content and such other content. Thus,for example, a Content-Monitoring Computer may be instructed to monitorcontent regarding a product (i.e., the selected content). Such contentmay be presented in response to a language preference indicated by auser. For example, content (including advertising, notices, menus, etc.)may be disseminated in a particular language in order to target userswho have indicated a weighting preference to be presented with contentin that language. Thus, for example, the invention permits directedcontent provision (including targeted advertising) to a group of userssharing a language demographic that may be relevant to the contentoriginator (e.g., governmental entity, commercial entity, organization,etc.). Similarly, the invention, through its capability to translatecontent from a first language into a second language permits contentprovision (including advertising) to be more easily accessed and used byusers sharing a different language demographic from that used by thecontent originator.

Such additional content may be query-specified, and/or may involve one,two or multiple topics. The Content-Monitoring Computer may then also beinstructed to monitor content regarding a particular service, event orlocation. By then instructing the Content-Monitoring Computer to comparethe identifier codings associated with Client Computers receiving orproviding both such contents, the Content-Monitoring Computer candiscern the number and relative percentage of the intersection of ClientComputers that are receiving or providing content about both therelevant product and such particular service, event or location. As anexample, by selecting to monitor “Apple® computer” and thencross-correlating with a proximity of “North Carolina” or “California,”one can discern the number or percentage of Client Computers concernedwith Apple® computers in North Carolina or California. By performingsuch selection repeatedly over time, one can discern how the associationvaries over time.

The capability of the Content-Monitoring Computers of the presentinvention thus differs significantly from those provided by socialreviewing networks such as Yelp® or Angie's List®, in being anonymous,automatic, dynamic, and correlatable.

Although Content-Monitoring Computers are primarily involved inmonitoring network content, Content-Monitoring Computers may also havethe ability to provide content to Client Computers of the network. Forexample, a Content-Monitoring Computer may respond to the monitoredcontent flow by providing inducements to other Client Computers (e.g.,coupons, free additional content, etc.) to motivate users to commentmore favorably about a service, event, location, product, etc.Similarly, the operators of the amusement park could use aContent-Monitoring Computer to provide Client Computers with informationabout less crowded areas of the park in order to direct park guests awayfrom more congested park areas.

VI. Exemplary User Interfaces of the Client Computers of the ComputerSystems of the Present Invention

The Client Computers of the present invention will preferably present toits user a graphical user interface that will comprise text input boxessufficient to permit a user to indicate the user's profile and/orpreferences in accessing and using a computer system of the presentinvention. Such profile and preferences may include, the user's actualname, a selected “username,” a selected password, preferences relatingto the desired type or types of weighting, and the parameter(s) of suchweighting (e.g., the time interval to be used in time-weighting, thedistance parameter to be used in proximity-weighting, etc.). Such a textinput screen is shown in FIG. 5. Additionally, such preferences mayinclude a user's privacy preferences, such as whether the user's actualname (or selected username, emoji, or avatar) is to be viewable by otherusers, or whether such user's content is to be anonymous to other users.The privacy preferences may additionally restrict the distribution of auser's content to one or more user-selected other users (“friends”).

Any of a wide variety of user interfaces may be employed to permit theuser to effectively perceive received content or provide content toother users. Such interfaces may be textual, such as a thread list, butmore preferably, an “atomic” user interface is employed, in which theuser by “grasping” one or more graphical elements (or selecting suchgraphical elements) (FIG. 6) of a touch-sensitive, or voice-responsive,screen is able to have the interface “zoom in” to see one or morepreviously smaller or previously-non-visible graphical elements,denoting a smaller universe of more selected content. Conversely, by“pinching” one or more of the graphical elements (or selecting suchgraphical elements), the user is able to have the interface “zoom out”to present additional content topics (FIG. 7). Preferably, the interfacewill permit the user to swipe across the graphical elements to causethem to revolve and thereby bring additional graphic elements into view.The graphical element(s) are preferably sized, colored, labeled, and/ortextured, etc. to indicate their relative weighting, such that, forexample, if time-weighting is applied, the graphical element associatedwith a topic of greater recency will be more prominently colored ordisplayed, etc. It is also preferred that the graphical element(s) aresized, colored, labeled, and/or textured, etc. to indicate the rate ofchange of a topic relative to such weighting. For example, the graphicalelements may be spheres whose diameter reflects the weighting of thecontent, and whose color reflects the rate of change of that topicrelative to such weighting. Alternatively, the thickness of the circularouter boundary of such spheres may vary in proportion to the rate ofchange of that topic relative to such weighting.

The interface will additionally preferably permit the user to draggraphical element(s), so that graphical elements representing topicshaving a user-perceived or user-defined relationship may be adjacent toone another or spaced relative to one another in a manner desired by theuser. FIG. 8 illustrates the invention by showing how such zooming maybe used to ultimately lead to the presentation of content, and shows anillustrative topic conversation among several attendees of a lecture.Initially the participants are commenting anonymously, however, “Anon28”and “Anon12” eventually reveal their identities to one another. As shownin FIG. 8, users may elect to provide textual content, image content orsound content.

Preferably, the user interface will permit the user to drag graphicelements to be placed onto a sorting graphic element (FIG. 9A) or tooverlap with one another (FIG. 9B) so as to permit the user to viewrelated content that relates to two or more topics. Still morepreferably, the user interface will permit the user to select graphicelements (which may then change color, or appearance to indicate suchselection), to thereby select to view related content that relates tothe topics of the selected graphical elements (FIG. 9C). Thus, thegraphical element labeled “Seattle,” as shown in FIGS. 9A-9C, includesmultiple topics all of which relate in some manner to Seattle, and thegraphical element labeled “Weather,” in FIGS. 9A-9C includes multipletopics all of which relate in some manner to weather anywhere. However,by dragging the “Seattle” and “Weather” graphical elements to a sortinggraphical element (shown as a star) a user may select to view contentinvolving the weather in Seattle (FIG. 9A). Alternatively, a user maydrag one graphic element (e.g., the “Seattle” graphical element) so thatit overlaps with another graphic element (e.g., the “Weather” graphicalelement) to access content relating to weather in Seattle (FIG. 9B).Alternatively, the user may “select” the graphical elements of interest(e.g., by touching such graphical elements). The interface thenpreferably alters the appearance of the selected elements to indicatetheir selection, and provides the user with content relating to topicsinvolving the selected graphical elements (e.g., Seattle weather; FIG.9C).

Particularly in conjunction with location-weighting, the user interfacemay comprise a map (such as a “heat map”) having graphical element(s)that are sized, colored, labeled, and/or textured, etc. to indicatetheir relative weighting. Thus, for example, a Client Computerpresenting a time-weighting map of an amusement park would present itsuser with a gradient of the currently most crowded areas of the park.FIG. 10A illustrates this aspect of the present invention, showing TownSquare and Future Land as being the most crowded sections of anamusement park. A user might then elect to visit other, less crowdedareas of the park. The user might then direct his/her Client Computer topresent a rank-weighting map of the amusement park, and thus be able toperceive a gradient of the areas of the park currently spurring the mostdiscussion. FIG. 10B illustrates this aspect of the present invention,showing that something interesting is happening in the Magic Land andAdventure Place sections of the park. The operators of the amusementpark could use a Content-Monitoring Computer to assess how such “heatmaps” of park traffic are changing over time, and thus be able to betterallocate park resources so as to alleviate congestion or foster improvedtraffic flow. Preferably, such heat map changes are provided to the userof the Content-Monitoring Computer in real time (e.g., as a pseudo-videoformed from rapidly updating static heat map images, or as a continuousvideo of traffic flow, etc.).

The user interface may exploit a camera or other optical image input, ifsuch is present on the Client Computer device. For example, the userinterface may use the camera of a camera-containing Client Computer toassociate content with a particular location, orientation or user. Thus,the user interface will appear to be an image of the surroundings (forexample, an augmented reality image) that comprises one or moreannotations relating to the location or orientation of a contentprovider and the nature of the provided content (FIG. 11). Clientcomputers of users that have provided permission to a receiving ClientComputer may be specifically localized on the user interface, so as tobe personally identified. Conversely, Client Computers of users thathave not granted such permission will preferably be localized only to anarea, so as to remain anonymous to the receiving Client Computer.

The user interface will preferably notate users having multipleapproving votes with a symbol (e.g., a star, thumbs up, etc.) denotingsuch approval. The user interface may additionally notate users havingmultiple disapproving votes with a symbol denoting such disapproval.

All publications and patents mentioned in this specification are hereinincorporated by reference to the same extent as if each individualpublication or patent application was specifically and individuallyindicated to be incorporated by reference in its entirety. While theinvention has been described in connection with specific embodimentsthereof, it will be understood that it is capable of furthermodifications and this application is intended to cover any variations,uses, or adaptations of the invention following, in general, theprinciples of the invention and including such departures from thepresent disclosure as come within known or customary practice within theart to which the invention pertains and as may be applied to theessential features hereinbefore set forth.

What is claimed is:
 1. A computer system for disseminating content amonginterconnected Client Computers, wherein said computer system comprises:a content-providing Client Computer (I) and a content-receiving ClientComputer (II) digitally interconnected with one another directly orthrough one or more other Client Computer(s) to form a distributedcommunications network; wherein: (A) said content-providing ClientComputer (I) is adapted to transmit a wireless and/or wired signal oftextual, image and/or tonal data to said content-receiving ClientComputer (II) either directly or through said one or more other ClientComputer(s) of said distributed communications network; (B) saidcontent-receiving Client Computer (II) is adapted to receive at least asubset of the transmitted signal of said content-providing ClientComputer (I) and to present at least a subset of said received data to auser of said content-receiving Client Computer (II); wherein the contentof said received or presented data adjusts in response to changes in:(i) a Favorability Value assigned by said content-providing ClientComputer (I); and/or (ii) a weighting preference assigned by saidcontent-receiving Client Computer (II); and/or (iii) where said ClientComputers (I) and (II) are interconnected to one another through one ormore other Client Computer(s), a Favorability Value assigned by one ormore of said other Client Computer(s); thereby disseminating contentacross said distributed network; and wherein said Favorability Value isdetermined by a Favorability Function that considers one or moreFavorability Parameter(s), with the proviso that when selectedFavorability Parameters include both vote and time, the FavorabilityFunction shall additionally consider one or more additional FavorabilityParameter(s).
 2. The computer system of claim 1, wherein saidcontent-receiving Client Computer (II) of said computer system storesreceived content in a Content Stack memory; wherein content stored insaid Content Stack memory rises in response to increases in FavorabilityValue of the content, and/or falls in the Content Stack memory inresponse to decreases in Favorability Value, such that the number oramount of content stored in the Content Stack memory remains withinavailable processing and bandwidth parameters.
 3. The computer system ofclaim 2, wherein said content-receiving Client Computer (II) of saidcomputer system presents a subset of said stored received content to itsuser, wherein said presented content is stored in a Presentation Stackmemory; wherein content stored in said Presentation Stack memory: (A)rises in the Presentation Stack memory in response to: (1) increasedproximity between said content-receiving Client Computer (II) and saidcontent-providing Client Computer (I); (2) increases in the FavorabilityValue of the content: (i) assigned by said content-providing ClientComputer (I); and/or (ii) assigned by said content-receiving ClientComputer (II); and/or (iii) where said Client Computers (I) and (II) areinterconnected to one another through one or more other ClientComputer(s), assigned by one or more of said other Client Computer(s);and/or (3) changes in weighting preferences applied by saidcontent-receiving Client Computer (II) that increase its user's desirefor such content; and/or (B) falls in the Presentation Stack memory inresponse to: (1) decreased proximity between said content-receivingClient Computer (II) and said content-providing Client Computer (I); (2)decreases in the Favorability Value of the content: (i) assigned by saidcontent-providing Client Computer (I); and/or (ii) assigned by saidcontent-receiving Client Computer (II); and/or (iii) where said ClientComputers (I) and (II) are interconnected to one another through one ormore other Client Computer(s), assigned by one or more of said otherClient Computer(s); and/or (3) changes in weighting preferences appliedby said content-receiving Client Computer (II) that decrease its user'sdesire for such content; such that the number or amount of contentstored in the Presentation Stack memory of Client Computer (II) remainswithin user-selected parameters.
 4. The computer system of claim 3,wherein said presented content is weighed based on one or more weightingpreference(s) selected from the group consisting of:proximity-weighting, rank-weighting, topic-weighting, query-weighting,time-weighting, location-weighting, locality-weighting, vote-weighting,and segment-weighting.
 5. The computer system of claim 1, wherein saidFavorability Parameter(s) comprise one or more of the FavorabilityParameter(s): dissemination, distance, hop-distance, and premium.
 6. Thecomputer system of claim 1, wherein said Client Computer-selected ornetwork-selected Favorability Parameters comprise two or more of theFavorability Parameters: vote, dissemination, distance, hop-distance andpremium.
 7. The computer system of claim 6, wherein a Client Computer ofsaid computer system votes to favor or disfavor a received content, orprovides related content, and provides said vote or said related contentto another Client Computer.
 8. The computer system of 1, wherein saidClient Computer-selected or network-selected Favorability Parameterscomprise two or more of the Favorability Parameters: dissemination,distance, hop-distance, time and premium.
 9. The computer system ofclaim 1, wherein said network additionally comprises aContent-Monitoring Computer.
 10. The computer system of claim 1, whereinsaid network comprises a Restricted Computer Network.
 11. The computersystem of claim 1, wherein said content-receiving Client Computer (II)presents said received data to said user using a graphical userinterface.
 12. The computer system of claim 1, wherein saidcontent-providing Client Computer (I) is additionally adapted to receiveat least a subset of a textual, image and/or tonal data signaltransmitted from another Client Computer, and wherein at least a subsetof said data received by said content-receiving Client Computer (II)directly or indirectly from said content-providing Client Computer (I),and presented to the user of said content-receiving Client Computer(II), was received by said content-providing Client Computer (I) fromsaid other Client Computer.
 13. The computer system of claim 1, whereinsaid content-receiving Client Computer (II) is additionally adapted totransmit a wireless and/or wired signal of textual, image and/or tonaldata to another Client Computer either directly or through said one ormore other Client Computer(s) of said distributed communicationsnetwork.
 14. The computer system of claim 1, wherein saidcontent-providing Client Computer (I), said content-receiving ClientComputer (II), or both said content-providing Client Computer (I), andsaid content-receiving Client Computer (II) is/are a smartphone, laptop,tablet, smartwatch or head-mounted display mobile device.
 15. Thecomputer system of claim 1, wherein said content-providing ClientComputer (I), said content-receiving Client Computer (II), or both saidcontent-providing Client Computer (I) and said content-receiving ClientComputer (II) is/are installed in a vehicle, watercraft or aircraft. 16.The computer system of claim 1, wherein said content-providing ClientComputer (I), said content-receiving Client Computer (II), or both saidcontent-providing Client Computer (I) and said content-receiving ClientComputer (II) is/are installed in a non-mobile structure.
 17. Thecomputer system of claim 1, wherein said content-providing ClientComputer (I), said content-receiving Client Computer (II), or both saidcontent-providing Client Computer (I) and said content-receiving ClientComputer (II) is/are installed in a drone or unattended device.
 18. Acomputer-implemented method for disseminating content amonginterconnected Client Computers, wherein said method enables the digitalinterconnection of a content-providing Client Computer (I) and acontent-receiving Client Computer (II), directly or through one or moreother Client Computer(s), to thereby form a distributed communicationsnetwork, wherein: (1) said content-providing Client Computer (I)transmits a wireless and/or wired signal of textual, image or tonal datasignal to said content-receiving Client Computer (II) either directly orthrough said one or more other Client Computer(s) of said distributedcommunications network; and (2) said content-receiving Client Computer(II) receives at least a subset of said data signal and presents atleast a subset of said received data to a user of said content-receivingClient Computer (II); wherein the content of said received or presenteddata adjusts in response to changes in: (i) a Favorability Valueassigned by said content-providing Client Computer (I); and/or (ii) aweighting preference assigned by said content-receiving Client Computer(II); and/or (iii) where said Client Computers (I) and (II) areinterconnected to one another through one or more other ClientComputer(s), a Favorability Value assigned by one or more of said otherClient Computer(s); thereby disseminating content across saiddistributed network; and wherein said Favorability Value is determinedby a Favorability Function that considers one or more FavorabilityParameter(s), with the proviso that when selected FavorabilityParameters include both vote and time, the Favorability Function shalladditionally consider one or more additional Favorability Parameter(s).19. The computer-implemented method of claim 18, wherein said methodpermits said content-receiving Client Computer (II) of said computersystem to store received content in a Content Stack memory; wherein saidmethod permits content stored in said Content Stack memory to rise inresponse to increases in Favorability Value of the content, and/or tofall in the Content Stack memory in response to decreases inFavorability Value, such that the number or amount of content stored inthe Content Stack memory remains within available processing andbandwidth parameters.
 20. The computer-implemented method of claim 19,wherein said method permits said content-receiving Client Computer (II)of said computer system to present a subset of said stored content toits user, wherein said presented content is stored in a PresentationStack memory; wherein said method permits content stored in saidPresentation Stack memory: (A) to rise in the Presentation Stack memoryin response to: (1) increased proximity between said content-receivingClient Computer (II) and said content-providing Client Computer (I); (2)increases in the Favorability Value of the content: (i) assigned by saidcontent-providing Client Computer (I); and/or (ii) assigned by saidcontent-receiving Client Computer (II); and/or (iii) where said ClientComputers (I) and (II) are interconnected to one another through one ormore other Client Computer(s), assigned by one or more of said otherClient Computer(s); and/or (3) changes in the weighting preferencesapplied by said content-receiving Client Computer (II) that increase itsuser's desire for such content; and/or (B) to fall in the PresentationStack memory in response to: (1) decreased proximity between saidcontent-receiving Client Computer (II) and said content-providing ClientComputer (I); (2) decreases in the Favorability Value of the content:(i) assigned by said content-providing Client Computer (I); and/or (ii)assigned by said content-receiving Client Computer (II); and/or (iii)where said Client Computers (I) and (II) are interconnected to oneanother through one or more other Client Computer(s), assigned by one ormore of said other Client Computer(s); and/or (3) changes in theweighting preferences applied by said content-receiving Client Computer(II) that decrease its user's desire for such content; such that thenumber or amount of content stored in the Presentation Stack memory ofthe Client Computer remains within user-selected parameters.
 21. Thecomputer-implemented method of claim 20, wherein said method permitssaid presented content to be weighted based on one or more weightingpreference(s) selected from the group consisting of:proximity-weighting, rank-weighting, topic-weighting, query-weighting,time-weighting, location-weighting, locality-weighting, vote-weighting,and segment-weighting.
 22. The computer-implemented method of claim 18,wherein said method permits said Client Computer-selected ornetwork-selected Favorability Parameters to comprise one or more of theFavorability Parameter(s): dissemination, distance, hop-distance andpremium.
 23. The computer-implemented method of claim 18, wherein saidmethod permits said Client Computer-selected or network-selectedFavorability Parameters to comprise two or more of the FavorabilityParameters: vote, dissemination, distance, hop-distance and premium. 24.The computer-implemented method of claim 23, wherein said method permitsa Client Computer of said computer system to: (A) (1) vote to favor ordisfavor a received content, or (2) contribute related content, and (B)provide said vote or said related content to another Client Computer.25. The computer-implemented method of claim 18, wherein said methodpermits said Client Computer-selected or network-selected FavorabilityParameters to comprise two or more of the Favorability Parameters:dissemination, distance, hop-distance, time and premium.
 26. Thecomputer-implemented method of claim 18, wherein said method permitssaid network to additionally comprise a Content-Monitoring Computer. 27.The computer-implemented method of claim 18, wherein said method permitssaid network to comprise a Restricted Computer Network.
 28. Thecomputer-implemented method of claim 18, wherein said content-receivingClient Computer (II) presents said received data to said user using agraphical user interface.
 29. The computer-implemented method of claim18 wherein said content-providing Client Computer (I) additionallyreceives at least a subset of a textual, image and/or tonal data signaltransmitted from another Client Computer, and wherein at least a subsetof said data received by said content-receiving Client Computer (II)directly or indirectly from said content-providing Client Computer (I),and presented to the user of said content-receiving Client Computer(II), was received by said content-providing Client Computer (I) fromsaid other Client Computer.
 30. The computer-implemented method of claim18 wherein said content-receiving Client Computer (II) additionallytransmits a textual, image and/or tonal data signal to another ClientComputer either directly or through said one or more other ClientComputer(s) of said distributed communications network.
 31. Thecomputer-implemented method of claim 18, wherein said content-providingClient Computer (I), said content-receiving Client Computer (II), orboth said content-providing Client Computer (I) and saidcontent-receiving Client Computer (II) is/are a smartphone, laptop,tablet, smartwatch or head-mounted display mobile device.
 32. Thecomputer-implemented method of claim 18, wherein said content-providingClient Computer (I), said content-receiving Client Computer (II), orboth said content-providing Client Computer (I) and saidcontent-receiving Client Computer (II) is/are installed in a vehicle,watercraft or aircraft.
 33. The computer-implemented method of claim 18,wherein said content-providing Client Computer (I), saidcontent-receiving Client Computer (II), or both said content-providingClient Computer (I) and said content-receiving Client Computer (II)is/are installed in a non-mobile structure.
 34. The computer-implementedmethod of claim 18, wherein said content-providing Client Computer (I),said content-receiving Client Computer (II), or both saidcontent-providing Client Computer (I) and said content-receiving ClientComputer (II) is/are installed in a drone or unattended device.